From 9e3a0cf7b659f8b575b688afa112a1263393bf81 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Mon, 22 Apr 2019 10:41:47 -0800 Subject: [PATCH 01/12] projections for rebuilding based on 2018 model, may 2019 --- examples/smbkc_18a/projections/README.md | 39 + examples/smbkc_18a/projections/gmacs.tpl | 6366 ++++++++++++++++++++++ 2 files changed, 6405 insertions(+) create mode 100644 examples/smbkc_18a/projections/README.md create mode 100644 examples/smbkc_18a/projections/gmacs.tpl diff --git a/examples/smbkc_18a/projections/README.md b/examples/smbkc_18a/projections/README.md new file mode 100644 index 00000000..987a5c48 --- /dev/null +++ b/examples/smbkc_18a/projections/README.md @@ -0,0 +1,39 @@ +# smbkc +model for smbkc for 2018a model framework and setup are pulled from 'gmacs' + +# Current model +from gmacs/examples/smbkc_18a/model_1 +was model 3 from 2018 assessment + +Use tpl file from Jie saved locally: +Local(C:\Users\kjpalof\Documents\SMBKC\breakpoint_SR\breakpoint_smbkc\model_1_2018a ) +Use this .tpl file to create gmacs.exe +also put this .tpl in projection folder + +# projections + +Projection + +1: mean recruitment 1978-2017 +2: ricker recruitment +3: B-H recruitment +4: mean recruitment 1996-2017 +5: mean recruitment 1996-2017, Bmsy 1996-2017 +6: mean recruitment 1999-2008 +7: mean recruitment 1989-2017 + +Versions: +a) no bycath mortality / no State harvest control policy +b) bycath mortality / State harvest control policy +c) no bycath mortality / no State harvest control policy +d) bycath mortality / State harvest control policy + + +# Summary of results +Bycatch mortality does NOT influence projection results + + + +# To do / Issues +More projection models? +Weighted combinations? \ No newline at end of file diff --git a/examples/smbkc_18a/projections/gmacs.tpl b/examples/smbkc_18a/projections/gmacs.tpl new file mode 100644 index 00000000..53a57d0b --- /dev/null +++ b/examples/smbkc_18a/projections/gmacs.tpl @@ -0,0 +1,6366 @@ +// ==================================================================================== // +// Gmacs: A generalized size-structured stock assessment modeling framework. +// +// Authors: gmacs development team at the +// Alaska Fisheries Science Centre, Seattle +// and the University of Washington. +// +// Info: https://github.com/seacode/gmacs Copyright (C) 2014-2019. All rights reserved. +// +// ACKNOWLEDGEMENTS: +// finacial support provided by NOAA and Bering Sea Fisheries Research Foundation. +// +// INDEXES: +// g/ig = group +// h = sex +// i = year +// j = time step (years) +// k = gear or fleet +// l = index for length class +// m = index for maturity state +// o = index for shell condition. +// +// OUTPUT FILES: +// gmacs.rep Main result file for reading into R etc +// gmacs.std Result file for reading into R etc +// gmacsall.out Result file for all all sorts of purposes. +// +// FOR DEBUGGING INPUT FILES: (for accessing easily with read_admb() function) +// gmacs_files_in.dat Which control and data files were specified for the current run +// gmacs_in.ctl Code-generated copy of control file content (useful for checking read) +// gmacs_in.dat Code-generated copy of data file content (useful for checking read) +// gmacs_in.prj Code-generated copy of projection file content (useful for checking read) +// +// TO ECHO INPUT +// checkfile.rep All of data read in +// +// ==================================================================================== // + + +DATA_SECTION + //friend_class gmacs_comm; + // |---------------------| + // | SIMULATION CONTROLS | + // |---------------------| + + int simflag; + int rseed; + LOC_CALCS + simflag = 0; + rseed = 0; + int opt,on; + + /** + * @brief command line option for simulating data. + **/ + if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-sim",opt))>-1 ) + { + simflag = 1; + rseed = atoi(ad_comm::argv[on+1]); + } + + if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-i",opt))>-1 ) + { + cout << "\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | CONTRIBUTIONS (code and intellectual) |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | Name: Organization: |\n"; + cout << " | James Ianelli NOAA-NMFS |\n"; + cout << " | D'Arcy Webber Quantifish |\n"; + cout << " | Steven Martell SeaState |\n"; + cout << " | Jack Turnock NOAA-NMFS |\n"; + cout << " | Jie Zheng ADF&G |\n"; + cout << " | Hamachan Hamazaki ADF&G |\n"; + cout << " | Athol Whitten University of Washington |\n"; + cout << " | André Punt University of Washington |\n"; + cout << " | Dave Fournier Otter Research |\n"; + cout << " | John Levitt Mathemetician |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << "\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | FINANCIAL SUPPORT |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | Financial support for this project was provided by the |\n"; + cout << " | National Marine Fisheries Service, the Bering Sea |\n"; + cout << " | Fisheries Research Foundation, ... |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << "\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | DOCUMENTATION |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | online api: http://seacode.github.io/gmacs/index.html |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << "\n"; + exit(1); + } + + // Command line option here to do retrospective analysis + if ( (on=option_match(ad_comm::argc,ad_comm::argv, "-retro", opt))>-1 ) + { + cout << "\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | Running retrospective model with " << ad_comm::argv[on+1] << " recent yrs removed |\n"; + cout << " +----------------------------------------------------------+\n"; + cout << " | YET TO BE IMPLEMENTED |\n"; + cout << " +----------------------------------------------------------+\n"; + exit(1); + } + END_CALCS + + // create a random number generator for projections + + // |------------------------| + // | DATA AND CONTROL FILES | + // |------------------------| + init_adstring datafile; + init_adstring controlfile; + init_adstring projectfile; + !! ad_comm::change_datafile_name(datafile); WriteFileName(datafile); WriteFileName(controlfile); WriteFileName(projectfile); + !! cout << "+----------------------+" << endl; + !! cout << "| Reading data file |" << endl; + !! cout << "+----------------------+" << endl; + + // |------------------| + // | MODEL DIMENSIONS | + // |------------------| + !! cout << " * Model dimensions" << endl; + init_int syr; ///> initial year + init_int nyr; ///> terminal year + init_int nseason; ///> time step (years) + init_int nfleet; ///> number of gears (including surveys and fisheries) + init_int nsex; ///> number of sexes + !! if (nsex != 1 & nsex != 2) + !! { cout << "nsex can only be 1 or 2; STOPPING" << endl; exit(1); } + init_int nshell; ///> number of shell conditions + init_int nmature; ///> number of maturity types + init_int nclass; ///> number of size-classes + + init_int season_recruitment; ///> Season that recruitment occurs (end of year before growth) + init_int season_growth; ///> Season that growth occurs (end of year after recruitment) + init_int season_ssb; ///> Season to calculate SSB (end of year) + init_int season_N; ///> Season to output N + + int n_grp; ///> number of sex/newshell/oldshell groups + int nlikes; ///> 1 2 3 4 5 + LOC_CALCS + n_grp = nsex * nshell * nmature; + nlikes = 5; ///> catch, cpue, size comps, recruits, molt increments + WRITEDAT(syr); WRITEDAT(nyr); WRITEDAT(nseason); + WRITEDAT(nfleet); WRITEDAT(nsex); WRITEDAT(nshell); WRITEDAT(nmature); WRITEDAT(nclass); + WRITEDAT(season_recruitment); WRITEDAT(season_growth); + WRITEDAT(season_ssb); WRITEDAT(season_N); + END_CALCS + + // Set up index pointers + ivector isex(1,n_grp); + ivector ishell(1,n_grp); + ivector imature(1,n_grp); + 3darray pntr_hmo(1,nsex,1,nmature,1,nshell); + LOC_CALCS + int h,m,o; + int hmo=1; + for ( h = 1; h <= nsex; h++ ) + for ( m = 1; m <= nmature; m++ ) + for ( o = 1; o <= nshell; o++ ) + { + isex(hmo) = h; + ishell(hmo) = o; + imature(hmo) = m; + pntr_hmo(h,m,o) = hmo++; + } + END_CALCS + init_vector size_breaks(1,nclass+1); + vector mid_points(1,nclass); + !!WRITEDAT(size_breaks); + + // |-------------------------------| + // | NATURAL MORTALITY | + // |-------------------------------| + !! cout << " * Natural mortality" << endl; + init_int m_prop_type; ///> 1 for vector by season; 2 for matrix by year and season + !! if (m_prop_type != 1 & m_prop_type != 2) + !! { cout << "m_prop_type can only be 1 or 2; STOPPING" << endl; exit(1); } + !! WRITEDAT(m_prop_type); + int m_dim; + LOC_CALCS + m_dim = 1; + if ( m_prop_type == 2 ) m_dim = nyr - syr + 1; + END_CALCS + init_matrix m_prop_in(1,m_dim,1,nseason); + !! WRITEDAT(m_prop_in); + matrix m_prop(syr,nyr,1,nseason); + LOC_CALCS + switch ( m_prop_type ) + { + // vector by season + case 1: + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + m_prop(i,j) = m_prop_in(1,j); + break; + // matrix by year and season + case 2: + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + m_prop(i,j) = m_prop_in(i-syr+1,j); + break; + } + for ( int i = syr; i <= nyr; i++ ) + if ( sum(m_prop(i)) > 1.0000001 || sum(m_prop(i)) < 0.999999 ) + { + cout << "Error: the proportion of natural mortality applied each season (in the .dat file) does not sum to 1! It sums to " << sum(m_prop(i)) << endl; + exit(1); + } + END_CALCS + + // |-------------| + // | FLEET NAMES | + // |-------------| + init_adstring name_read_flt; + init_adstring name_read_srv; + !! WRITEDAT(name_read_flt); + !! WRITEDAT(name_read_srv); + + // |-------------| + // | FLEET TYPES | + // |-------------| + init_ivector season_type(1,nseason); ///> Set to 0 for discrete; 1 for continuous + !! WRITEDAT(season_type); + + // |--------------| + // | CATCH SERIES | + // |--------------| + !! cout << " * Catch data" << endl; + init_int nCatchDF; + init_ivector nCatchRows(1,nCatchDF); + init_3darray dCatchData(1,nCatchDF,1,nCatchRows,1,11); // array of catch data + !! WRITEDAT(nCatchDF); WRITEDAT(nCatchRows); WRITEDAT(dCatchData); + + matrix obs_catch(1,nCatchDF,1,nCatchRows); + matrix obs_effort(1,nCatchDF,1,nCatchRows); + 3darray dCatchData_out(1,nCatchDF,syr,nyr,1,11); + matrix obs_catch_out(1,nCatchDF,syr,nyr); + + matrix catch_cv(1,nCatchDF,1,nCatchRows); + matrix catch_dm(1,nCatchDF,1,nCatchRows); + matrix catch_mult(1,nCatchDF,1,nCatchRows); + + LOC_CALCS + for ( int k = 1; k <= nCatchDF; k++ ) + { + catch_mult(k) = column(dCatchData(k),9); + obs_catch(k) = column(dCatchData(k),5); + catch_cv(k) = column(dCatchData(k),6); + catch_dm(k) = column(dCatchData(k),11); + obs_catch(k) = elem_prod(obs_catch(k), catch_mult(k)); ///> rescale catch by multiplier + obs_effort(k) = column(dCatchData(k),10); + } + ECHO(obs_catch); + ECHO(catch_cv); + END_CALCS + + // From the catch series determine the number of fishing mortality rate parameters that need to be estimated. Note that there is a number of combinations which require an F to be estimated. + ivector nFparams(1,nfleet); ///> The number of deviations required for each fleet + ivector nYparams(1,nfleet); ///> The number of deviations for female Fs + 3darray fhit(syr,nyr,1,nseason,1,nfleet); ///> set to 1 for present; 0 for absent + 3darray yhit(syr,nyr,1,nseason,1,nfleet); ///> set to 1 for present; 0 for absent + matrix dmr(syr,nyr,1,nfleet); ///> discard mortality - has to be the same for all catch series for eac fleet + + LOC_CALCS + nFparams.initialize(); + nYparams.initialize(); + fhit.initialize(); + yhit.initialize(); + dmr.initialize(); + for ( int k = 1; k <= nCatchDF; k++ ) + { + for ( int i = 1; i <= nCatchRows(k); i++ ) + { + int y = dCatchData(k)(i,1); ///> year + int j = dCatchData(k)(i,2); ///> season + int g = dCatchData(k)(i,3); ///> fleet + int h = dCatchData(k)(i,4); ///> sex + + // Check whether the fleet is instantaneous but natural mortality in the relevant period is not zero! + if (season_type(j) == INSTANT_F & m_prop(y,j) > 0) + { + cout << "The proportion of M should be zero for season " << j << " in year " << y << endl; + exit(1); + } + + if ( !fhit(y,j,g) ) + { + fhit(y,j,g) ++; + nFparams(g) ++; + dmr(y,g) = catch_dm(k)(i); + } + if ( !yhit(y,j,g) && h == 2 ) + { + yhit(y,j,g) ++; + nYparams(g) ++; + dmr(y,g) = catch_dm(k)(i); + } + } + } + ECHO(nFparams); ECHO(nYparams); ECHO(fhit); ECHO(yhit); ECHO(dmr); + + // Check for errors in discard rate assumptions + for ( int k = 1; k <= nCatchDF; k++ ) + for ( int i = 1; i <= nCatchRows(k); i++ ) + { + int y = dCatchData(k)(i,1); ///> year + int g = dCatchData(k)(i,3); ///> fleet + if (catch_dm(k)(i) != dmr(y,g)) + { cout << "ERROR: discards rates do not match: year " << y << " " << endl; exit(1); } + } + + // Create the dCatchData_out object for output and plotting in R, this object simply fills in the years that don't have data with zero catch + dCatchData_out.initialize(); + obs_catch_out.initialize(); + for ( int k = 1; k <= nCatchDF; k++ ) + for ( int i = syr; i <= nyr; i++ ) + { + dCatchData_out(k,i,1) = i; // Year + int j = 1; + for ( int ii = 1; ii <= nCatchRows(k); ii++ ) + { + if ( i == dCatchData(k,ii,1) ) // year index + { + j = ii; + obs_catch_out(k,i) = dCatchData(k,ii,5); // Obs + dCatchData_out(k,i,5) = dCatchData(k,ii,5); // Obs + dCatchData_out(k,i,6) = dCatchData(k,ii,6); // CV + dCatchData_out(k,i,9) = dCatchData(k,j,9); // Mult + dCatchData_out(k,i,10) = dCatchData(k,j,10); // Effort + dCatchData_out(k,i,11) = dCatchData(k,j,11); // Discard mortality + } + } + // Replicate these variables + dCatchData_out(k,i,2) = dCatchData(k,j,2); // Season + dCatchData_out(k,i,3) = dCatchData(k,j,3); // Fleet + dCatchData_out(k,i,4) = dCatchData(k,j,4); // Sex + dCatchData_out(k,i,7) = dCatchData(k,j,7); // Type + dCatchData_out(k,i,8) = dCatchData(k,j,8); // Units + } + END_CALCS + + // |----------------------------| + // | RELATIVE ABUNDANCE INDICES | + // |----------------------------| + !! cout << " * Abundance data" << endl; + init_int nSurveys; ///> Number of survey series + init_ivector nSurveyRows(1,nSurveys); + init_3darray dSurveyData(1,nSurveys,1,nSurveyRows,1,7); + matrix obs_cpue(1,nSurveys,1,nSurveyRows); + matrix cpue_cv(1,nSurveys,1,nSurveyRows); + matrix cpue_sd(1,nSurveys,1,nSurveyRows); + matrix cpue_cv_add(1,nSurveys,1,nSurveyRows); + LOC_CALCS + for ( int k = 1; k <= nSurveys; k++ ) + { + obs_cpue(k) = column(dSurveyData(k),5); + cpue_cv(k) = column(dSurveyData(k),6); + cpue_sd(k) = sqrt(log(1.0 + square(cpue_cv(k)))); + } + WRITEDAT(nSurveys); WRITEDAT(nSurveyRows); WRITEDAT(dSurveyData); + ECHO(obs_cpue); ECHO(cpue_cv); ECHO(cpue_sd); + END_CALCS + + // |-----------------------| + // | SIZE COMPOSITION DATA | + // |-----------------------| + !! cout << " * Size composition data" << endl; + init_int nSizeComps_in; ///> Number of series of size-comp data + init_ivector nSizeCompRows_in(1,nSizeComps_in); ///> Rows of data for each series + init_ivector nSizeCompCols_in(1,nSizeComps_in); ///> Number of size-class bins + init_3darray d3_SizeComps_in(1,nSizeComps_in,1,nSizeCompRows_in,-7,nSizeCompCols_in); + + 3darray d3_obs_size_comps_in(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); + matrix size_comp_sample_size_in(1,nSizeComps_in,1,nSizeCompRows_in); + matrix size_comp_year_in(1,nSizeComps_in,1,nSizeCompRows_in); + + LOC_CALCS + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + dmatrix tmp = trans(d3_SizeComps_in(kk)).sub(1,nSizeCompCols_in(kk)); + d3_obs_size_comps_in(kk) = trans(tmp); + size_comp_sample_size_in(kk) = column(d3_SizeComps_in(kk),0); + size_comp_year_in(kk) = column(d3_SizeComps_in(kk),-7); + } + WRITEDAT(nSizeComps_in); WRITEDAT(nSizeCompRows_in); WRITEDAT(nSizeCompCols_in); WRITEDAT(d3_SizeComps_in); + ECHO(d3_obs_size_comps_in); + END_CALCS + + // |-----------------------| + // | GROWTH INCREMENT DATA | + // |-----------------------| + !! cout << " * Growth data" << endl; + init_int GrowthObsType; ///> Type of observation (increment or change in size-class) + !! if (GrowthObsType != NOGROWTH_DATA & GrowthObsType != GROWTHINC_DATA & GrowthObsType != GROWTHCLASS_DATA) + !! { cout << "GrowthObsType can only be 0,1 or 2; STOPPING" << endl; exit(1); } + init_int nGrowthObs; ///> Number of data points + int NGrowthInputs; + !!if (GrowthObsType==GROWTHINC_DATA) NGrowthInputs = 4; else NGrowthInputs = 6; + init_matrix dGrowthData(1,nGrowthObs,1,NGrowthInputs); + + vector dPreMoltSize(1,nGrowthObs); + ivector iMoltIncSex(1,nGrowthObs); + vector dMoltInc(1,nGrowthObs); + vector dMoltIncCV(1,nGrowthObs); + vector mle_alpha(1,nsex); + vector mle_beta(1,nsex); + ivector iMoltInitSizeClass(1,nGrowthObs); + ivector iMoltEndSizeClass(1,nGrowthObs); + ivector iMoltTimeAtLib(1,nGrowthObs); + ivector iMoltTrans(1,nGrowthObs); + ivector iMoltSampSize(1,nGrowthObs); + ivector MaxGrowTimeLibSex(1,nsex) + !! MaxGrowTimeLibSex.initialize(); + int MaxGrowTimeLib; + !! MaxGrowTimeLib = 0; + LOC_CALCS + if (GrowthObsType==GROWTHINC_DATA) + { + dPreMoltSize = column(dGrowthData,1); + iMoltIncSex = ivector(column(dGrowthData,2)); + dMoltInc = column(dGrowthData,3); + dMoltIncCV = column(dGrowthData,4); + + dvector xybar(1,nsex); + dvector xx(1,nsex); + dvector xbar(1,nsex); + dvector ybar(1,nsex); + ivector nh(1,nsex); + + nh.initialize(); + xybar.initialize(); + xbar.initialize(); + ybar.initialize(); + xx.initialize(); + + // MLE estimates for alpha and beta for the linear growth increment model. + if ( nGrowthObs > 0 ) + { + for ( int i = 1; i <= nGrowthObs; i++ ) + { + int h = iMoltIncSex(i); + nh(h)++; + xybar(h) += dPreMoltSize(i) * dMoltInc(i); + xbar(h) += dPreMoltSize(i); + ybar(h) += dMoltInc(i); + xx(h) += square(dPreMoltSize(i)); + } + for ( h = 1; h <= nsex; h++ ) + { + xybar(h) /= nh(h); + xbar(h) /= nh(h); + ybar(h) /= nh(h); + xx(h) /= nh(h); + double slp = (xybar(h) - xbar(h)*ybar(h)) / (xx(h) - square(xbar(h))); + double alp = ybar(h) - slp*xbar(h); + mle_alpha(h) = alp; + mle_beta(h) = -slp; + } + } + } + if (GrowthObsType==GROWTHCLASS_DATA) + { + int iclass; + int h2; + iMoltIncSex = ivector(column(dGrowthData,2)); + iMoltTimeAtLib = ivector(column(dGrowthData,4)); + iMoltTrans = ivector(column(dGrowthData,5)); + iMoltSampSize = ivector(column(dGrowthData,6)); + for (int i = 1; i <= nGrowthObs; i++) + { + h2 = iMoltIncSex(i); + iclass = -1; + for (int j = 1; j <=nclass; j++) + if (dGrowthData(i,1) >= size_breaks(j) & dGrowthData(i,1) < size_breaks(j+1)) iclass = j; + iMoltInitSizeClass(i) = iclass; + if (iclass == -1) cout << "Warning - release size out of range" << endl; + iclass = -1; + for (int j = 1; j <=nclass; j++) + if (dGrowthData(i,3) >= size_breaks(j) & dGrowthData(i,3) < size_breaks(j+1)) iclass = j; + iMoltEndSizeClass(i) = iclass; + if (iclass == -1) cout << "Warning - recapture size out of range" << endl; + + if (MaxGrowTimeLibSex(h2) < iMoltTimeAtLib(i)) MaxGrowTimeLibSex(h2) = iMoltTimeAtLib(i); + if (MaxGrowTimeLib < iMoltTimeAtLib(i)) MaxGrowTimeLib = iMoltTimeAtLib(i); + } + } + WRITEDAT(GrowthObsType); WRITEDAT(nGrowthObs); WRITEDAT(dGrowthData); + if (GrowthObsType==GROWTHINC_DATA) + { ECHO(dPreMoltSize); ECHO(iMoltIncSex); ECHO(dMoltInc); ECHO(dMoltIncCV); } + if (GrowthObsType==GROWTHCLASS_DATA) + { ECHO(iMoltIncSex);ECHO(iMoltInitSizeClass); ECHO(iMoltEndSizeClass); ECHO(iMoltTimeAtLib); ECHO(iMoltSampSize);} + + END_CALCS + + // |------------------| + // | END OF DATA FILE | + // |------------------| + init_int eof; + !! WRITEDAT(eof); + !! if ( eof != 9999 ) {cout << "Error reading data" << endl; exit(1);} + !! cout << "end of data section" << endl; + +// ================================================================================================ +// ================================================================================================ + + // |----------------------------| + // | LEADING PARAMETER CONTROLS | + // |----------------------------| + !! ad_comm::change_datafile_name(controlfile); + !! cout << "+----------------------+" << endl; + !! cout << "| Reading control file |" << endl; + !! cout << "+----------------------+" << endl; + + !! cout << " * Key parameter controls" << endl; + init_int ntheta; ///> Number of leading parameters (guestimated) + init_matrix theta_control(1,ntheta,1,7); ///> The speciciations for the controls + !! WriteCtl(ntheta); WriteCtl(theta_control); + + vector theta_ival(1,ntheta); + vector theta_lb(1,ntheta); + vector theta_ub(1,ntheta); + ivector theta_phz(1,ntheta); + LOC_CALCS + theta_ival = column(theta_control,1); + theta_lb = column(theta_control,2); + theta_ub = column(theta_control,3); + theta_phz = ivector(column(theta_control,4)); + END_CALCS + + // |-------------------| + // | CUSTOM INPUT DATA | + // |-------------------| + + init_int bUseCustomNaturalMortality; ///> Select custom M options + !! WriteCtl( bUseCustomNaturalMortality); + !! if (bUseCustomNaturalMortality != NO_CUSTOM_M & bUseCustomNaturalMortality != WITH_CUSTOM_M) + !! { cout << "Custom Natural Mortality can only be 0 or 1; STOPPING" << endl; exit(1); } + matrix CustomNaturalMortality(1,nsex,syr,nyr); + !!if (bUseCustomNaturalMortality==WITH_CUSTOM_M) + !! { + !! for (int h=1;h<=nsex;h++) + !! for (int i=syr;i<=nyr;i++) + !! *(ad_comm::global_datafile) >> CustomNaturalMortality(h,i); + !! WriteCtl( CustomNaturalMortality); + !! } + + // |-----------| + // | ALLOMETRY | + // |-----------| + !! cout << " * Allometry" << endl; + init_int lw_type; ///> length-weight type/method (i.e. provide parameters or a vector) + !! WriteCtl(lw_type); + !! if (lw_type < 1 | lw_type > 3) + !! { cout << "length-weight type can only be 1,2 or 3; STOPPING" << endl; exit(1); } + int lw_dim; + LOC_CALCS + lw_dim = nsex; + if ( lw_type == LW_MATRIX ) lw_dim = nsex * (nyr+1 - syr + 1); + END_CALCS + vector lw_alfa(1,nsex); + vector lw_beta(1,nsex); + matrix mean_wt_in(1,lw_dim,1,nclass); + 3darray mean_wt(1,nsex,syr,nyr+1,1,nclass); ///> Ultimate array for this + + LOC_CALCS + if (lw_type==LW_RELATIONSHIP) + { + for (int h=1;h<=nsex;h++) *(ad_comm::global_datafile) >> lw_alfa(h); + for (int h=1;h<=nsex;h++) *(ad_comm::global_datafile) >> lw_beta(h); + WriteCtl(lw_alfa); WriteCtl(lw_beta); + } + if (lw_type==LW_VECTOR || lw_type==LW_MATRIX) + { + for (int I=1;I<=lw_dim;I++) + for ( int l = 1; l <= nclass; l++ ) *(ad_comm::global_datafile) >> mean_wt_in(I,l); + WriteCtl(mean_wt_in); + } + mid_points = size_breaks(1,nclass) + 0.5 * first_difference(size_breaks); + switch ( lw_type ) + { + // allometry + case LW_RELATIONSHIP: + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr+1; i++ ) + mean_wt(h,i) = lw_alfa(h) * pow(mid_points, lw_beta(h)); + break; + // vector by sex + case LW_VECTOR: + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr+1; i++ ) + for ( int l = 1; l <= nclass; l++ ) + mean_wt(h,i,l) = mean_wt_in(h,l); + break; + // matrix by sex + case LW_MATRIX: + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr+1; i++ ) + for ( int l = 1; l <= nclass; l++ ) + mean_wt(h,i,l) = mean_wt_in(i-syr+1,l); + break; + } + ECHO(mean_wt); + END_CALCS + + // |-----------------------------------| + // | FECUNDITY FOR MMB/MMA CALCULATION | + // |-----------------------------------| + !! cout << " * Maturity definition" << endl; + init_matrix maturity(1,nsex,1,nclass); + !! WriteCtl(maturity); + !! cout << " * Legal definition" << endl; + init_matrix legal(1,nsex,1,nclass); + !! WriteCtl(legal); + + // |----------------------------| + // | GROWTH PARAMETER CONTROLS | + // |----------------------------| + !! cout << " * Growth parameter controls" << endl; + init_int bUseCustomGrowthMatrix; + !! WriteCtl(bUseCustomGrowthMatrix); + !! if (bUseCustomGrowthMatrix < 1 | bUseCustomGrowthMatrix > 8) + !! { cout << "growth matrix type can only be 1,2 or 3; STOPPING" << endl; exit(1); } + + init_int bUseGrowthIncrementModel; + !! if (bUseCustomGrowthMatrix == GROWTH_VARYK) bUseGrowthIncrementModel = GROWTH_VARYK; + !! if (bUseCustomGrowthMatrix == GROWTH_VARYLINF) bUseGrowthIncrementModel = GROWTH_VARYLINF; + !! if (bUseCustomGrowthMatrix == GROWTH_VARYKLINF) bUseGrowthIncrementModel = GROWTH_VARYKLINF; + !! WriteCtl(bUseGrowthIncrementModel); + + init_int bUseCustomMoltProbility; + !! WriteCtl(bUseCustomMoltProbility); + !! if (bUseCustomGrowthMatrix == GROWTH_FIXEDSIZETRANS & bUseGrowthIncrementModel != FIXED_PROB_MOLT) + !! { + !! cout << "If the custom growth model = 1 molt probability must be 1; STOPPING" << endl; exit(1); + !! } + + init_ivector nSizeSex(1,nsex); ///> Number of size-classes by sex + !!WriteCtl(nSizeSex); + // Check at least one matches nclass + !! if (max(nSizeSex)!=nclass) { cout << "One of the maximum size-classes needs to match nclass; STOPPING" << endl; exit(1); } + init_ivector nSizeClassRec(1,nsex); ///> Maximum of size-classes to which recruitment must occur + !!WriteCtl(nSizeClassRec); + + int maxSizeIncVaries; + init_ivector nSizeIncVaries(1,nsex); ///> Number of blocks of growth matrix parameters + !!WriteCtl(nSizeIncVaries); + ivector nSizeIncChanges(1,nsex); ///> Number of CHANGES in growth matrix (usually zero) + !! nSizeIncChanges = nSizeIncVaries-1; + !! maxSizeIncVaries = max(nSizeIncVaries); + init_imatrix iYrsSizeIncChanges(1,nsex,1,nSizeIncChanges); ///> Years with changes in the growth matrix + !!WriteCtl(iYrsSizeIncChanges); + + init_ivector nMoltVaries(1,nsex); ///> Number of blocks of molt probability + ivector nMoltChanges(1,nsex); ///> Number of CHANGES in molt probability + !! WriteCtl(nMoltVaries); + !! nMoltChanges = nMoltVaries-1; + init_imatrix iYrsMoltChanges(1,nsex,1,nMoltChanges); ///> Years with changes in molt probability + !!WriteCtl(iYrsMoltChanges); + + init_int BetaParRelative; ///> Are the beta parameters relative to a base level + !!WriteCtl(BetaParRelative); + + imatrix iYrIncChanges(1,nsex,syr,nyr); + LOC_CALCS + for (int h=1;h<=nsex;h++) + { + for (int y=syr;y<=nyr;y++) iYrIncChanges(h,y) = 1; + for (int j=1;j<=nSizeIncChanges(h);j++) + if (j!=nSizeIncChanges(h)) + for (int y=iYrsSizeIncChanges(h,j);y<=iYrsSizeIncChanges(h,j+1);y++) iYrIncChanges(h,y) = j+1; + else + for (int y=iYrsSizeIncChanges(h,j);y<=nyr;y++) iYrIncChanges(h,y) = j+1; + } + ECHO(iYrIncChanges); + END_CALCS + + int nGrwth; + int nSizeIncPar; + !! nGrwth = 0; nSizeIncPar = 0; + !! for (int h=1;h<=nsex;h++) + !! { + !! if (bUseGrowthIncrementModel==LINEAR_GROWTHMODEL) nSizeIncPar += nSizeIncVaries(h) * 3; + !! if (bUseGrowthIncrementModel==INDIVIDUAL_GROWTHMODEL1) nSizeIncPar += nSizeIncVaries(h) * (nclass+1); + !! if (bUseGrowthIncrementModel==INDIVIDUAL_GROWTHMODEL2) nSizeIncPar += nSizeIncVaries(h) * (nclass+1); + !! if (bUseGrowthIncrementModel==GROWTH_VARYK) nSizeIncPar += nSizeIncVaries(h) * 3; + !! if (bUseGrowthIncrementModel==GROWTH_VARYLINF) nSizeIncPar += nSizeIncVaries(h) * 3; + !! if (bUseGrowthIncrementModel==GROWTH_VARYKLINF) nSizeIncPar += nSizeIncVaries(h) * 4; + !! if (bUseCustomMoltProbility==LOGISTIC_PROB_MOLT) nGrwth += nMoltVaries(h) * 2; + !! } + !! nGrwth += nSizeIncPar; + !! ECHO(nGrwth); + + init_matrix Grwth_control(1,nGrwth,1,7); ///> Growth parameters + !! WriteCtl(Grwth_control); + + vector Grwth_ival(1,nGrwth); + vector Grwth_lb(1,nGrwth); + vector Grwth_ub(1,nGrwth); + ivector Grwth_phz(1,nGrwth); + LOC_CALCS + Grwth_ival = column(Grwth_control,1); + Grwth_lb = column(Grwth_control,2); + Grwth_ub = column(Grwth_control,3); + Grwth_phz = ivector(column(Grwth_control,4)); + END_CALCS + + 4darray CustomGrowthMatrix(1,nsex,1,maxSizeIncVaries,1,nclass,1,nclass); ///> Custom growth matrix or size-transition matrix (read in) + !!if (bUseCustomGrowthMatrix==GROWTH_FIXEDGROWTHTRANS || bUseCustomGrowthMatrix==GROWTH_FIXEDSIZETRANS) + !! { + !! for (int h=1;h<=nsex;h++) + !! for (int i=1;i<=nSizeIncVaries(h);i++) + !! { + !! for (int class1=1;class1<= nSizeSex(h);class1++) + !! for (int class2=1;class2<= nSizeSex(h);class2++) + !! *(ad_comm::global_datafile) >> CustomGrowthMatrix(h,i,class2,class1); + !! WriteCtl( trans(CustomGrowthMatrix(h,i)) ); + !! } + !! } + + 3darray CustomMoltProbabilityMatrix(1,nsex,1,nMoltVaries,1,nclass); ///> Custom molt probaility (read in) + !!if (bUseCustomMoltProbility==FIXED_PROB_MOLT) + !! { + !! for (int h=1;h<=nsex;h++) + !! for (int i=1;i<=nSizeIncVaries(h);i++) + !! for (int l=1;l<= nSizeSex(h);l++) + !! *(ad_comm::global_datafile) >> CustomMoltProbabilityMatrix(h,i,l); + !! WriteCtl(CustomMoltProbabilityMatrix); + !! } + + // |--------------------------------| + // | SELECTIVITY PARAMETER CONTROLS | + // |--------------------------------| + !! cout << " * Selectivity parameter controls" << endl; + int nslx; //> number of selectivities (gears x blocks selectivity + gears * blocks retained) + int nslx_pars; //> number of selectivity parameters in total + int nslx_rows_in; //> number of selectivity rows + int nslx_cols_in; //> number of selectivity columns + !! nslx_cols_in = 13; //> number of columns in the control file to be read in + + init_ivector slx_nsel_period_in(1,nfleet); //> number of selex time periods + init_ivector slx_bsex_in(1,nfleet); //> boolian for sex-specific selex + init_imatrix slx_type_in(1,nsex,1,nfleet); //> integer for selectivity type (e.g. logistic, double normal) + init_ivector slx_include_in(1,nfleet); //> insertion of fleet in another + + init_ivector ret_nret_period_in(1,nfleet); //> number of retention time periods + init_ivector ret_bsex_in(1,nfleet); //> boolian for sex-specific retention + init_imatrix ret_type_in(1,nsex,1,nfleet); //> integer for retention type (e.g. logistic, double normal) + init_imatrix slx_nret(1,nsex,1,nfleet); //> boolian for rentention/discard + + LOC_CALCS + WriteCtl(slx_nsel_period_in); + WriteCtl(slx_bsex_in); + WriteCtl(slx_type_in); + WriteCtl(slx_include_in); + WriteCtl(ret_nret_period_in); + WriteCtl(ret_bsex_in); + WriteCtl(ret_type_in); + WriteCtl(slx_nret); + // Work out how many selectivities we are dealing with, nfleet * nsex, plus any additonal sex-specific or time period selectivities + nslx = 0; + for ( int k = 1; k <= nfleet; k++ ) + { + nslx += slx_nsel_period_in(k) * (1 + slx_bsex_in(k)); + nslx += ret_nret_period_in(k) * (1 + ret_bsex_in(k)); + } + + // Work out how many rows are in the selectivity/retention control inputs matrix based on the type of each selectivity and the number of blocks + nslx_rows_in = 0; + for ( int k = 1; k <= nfleet; k++ ) + { + // Selectivity + for ( int h = 1; h <= slx_bsex_in(k)+1; h++ ) + { + switch ( slx_type_in(h,k) ) + { + case SELEX_PARAMETRIC: ///> parametric + nslx_rows_in += nclass * slx_nsel_period_in(k); + break; + case SELEX_COEFFICIENTS: ///> coefficients + nslx_rows_in += (nclass - 0) * slx_nsel_period_in(k); + break; + case SELEX_STANLOGISTIC: ///> logistic has 2 parameters + nslx_rows_in += 2 * slx_nsel_period_in(k); + break; + case SELEX_5095LOGISTIC: ///> logistic95 has 2 parameters + nslx_rows_in += 2 * slx_nsel_period_in(k); + break; + case SELEX_DOUBLENORM: ///> double normal has 3 parameters + nslx_rows_in += 3 * slx_nsel_period_in(k); + break; + case SELEX_UNIFORM1: ///> uniform has 1 parameter + nslx_rows_in += 1 * slx_nsel_period_in(k); + break; + case SELEX_UNIFORM0: ///> uniform has 1 parameter + nslx_rows_in += 1 * slx_nsel_period_in(k); + break; + default: + cout << "Selectvity type out of bounds" << endl; + exit(1); + break; + } + } + // Retention + for ( int h = 1; h <= ret_bsex_in(k)+1; h++ ) + { + switch ( ret_type_in(h,k) ) + { + case SELEX_PARAMETRIC: ///> parametric + nslx_rows_in += nclass * ret_nret_period_in(k); + break; + case SELEX_COEFFICIENTS: ///> coefficients + nslx_rows_in += (nclass - 0) * ret_nret_period_in(k); + break; + case SELEX_STANLOGISTIC: ///> logistic + nslx_rows_in += 2 * ret_nret_period_in(k); + break; + case SELEX_5095LOGISTIC: ///> logistic95 + nslx_rows_in += 2 * ret_nret_period_in(k); + break; + case SELEX_DOUBLENORM: ///> double normal + nslx_rows_in += 3 * ret_nret_period_in(k); + break; + case SELEX_UNIFORM1: ///> uniform has 1 parameter + nslx_rows_in += 1 * ret_nret_period_in(k); + break; + case SELEX_UNIFORM0: ///> uniform has 1 parameter + nslx_rows_in += 1 * ret_nret_period_in(k); + break; + default: + cout << "Retention type out of bounds" << endl; + exit(1); + break; + } + } + } + END_CALCS + + init_matrix slx_control_in(1,nslx_rows_in,1,nslx_cols_in); + !! WriteCtl(slx_control_in); + + imatrix slx_control(1,nslx,1,nslx_cols_in); + ivector slx_indx(1,nslx); ///> index for the first parameter for each gear type + ivector slx_gear(1,nslx); ///> index the gear type + ivector slx_type(1,nslx); ///> the type of selectivity function + ivector slx_isex(1,nslx); ///> 0 = males and females, 1 = males only, 2 = females only + ivector slx_styr(1,nslx); ///> period start year + ivector slx_edyr(1,nslx); ///> period end year + ivector slx_cols(1,nslx); + ivector slx_npar(1,nslx); ///> parameters by pattern + ivector slx_incl(1,nslx); + + LOC_CALCS + // Work out the type of each selectivity and place in the ivector slx_type + int kk = 1; + for ( int k = 1; k <= nfleet; k++ ) + { + int hh = 1 + slx_bsex_in(k); + for ( int h = 1; h <= hh; h++ ) + for ( int i = 1; i <= slx_nsel_period_in(k); i++ ) + { + slx_type(kk) = slx_type_in(h,k); + slx_incl(kk) = slx_include_in(k); + kk ++; + } + } + for ( int k = 1; k <= nfleet; k++ ) + { + int hh = 1 + ret_bsex_in(k); + for ( int h = 1; h <= hh; h++ ) + for ( int i = 1; i <= ret_nret_period_in(k); i++ ) + { + slx_type(kk) = ret_type_in(h,k); + kk ++; + } + } + // count up number of parameters required + slx_cols.initialize(); + for ( int k = 1; k <= nslx; k++ ) + { + switch ( slx_type(k) ) + { + case SELEX_PARAMETRIC: ///> parametric + slx_cols(k) = nclass; slx_npar(k) = nclass; + break; + case SELEX_COEFFICIENTS: ///> coefficients + slx_cols(k) = nclass - 0; slx_npar(k) = nclass - 0; + break; + case SELEX_STANLOGISTIC: ///> logistic + slx_cols(k) = 2; slx_npar(k) = 2; + break; + case SELEX_5095LOGISTIC: ///> logistic95 + slx_cols(k) = 2; slx_npar(k) = 2; + break; + case SELEX_DOUBLENORM: ///> double normal + slx_cols(k) = 3; slx_npar(k) = 3; + break; + case SELEX_UNIFORM1: ///> constant 1 + slx_cols(k) = 1; slx_npar(k) = 1; + break; + case SELEX_UNIFORM0: ///> constant 0 + slx_cols(k) = 1; slx_npar(k) = 1; + break; + } + } + + // slx_indx is an ivector of the index for the first parameter of each nslx in the slx_control_in matrix + kk = 1; + for ( int k = 1; k <= nslx; k++ ) + { + slx_indx(k) = kk; + kk += slx_cols(k); + } + // Store a version of the control file that only records the first parameter of each selectivity type - this is useful for R plots. Also extract vectors of elements from this matrix + for ( int k = 1; k <= nslx; k++ ) + { + int kk = slx_indx(k); + for ( int i = 1; i <= nslx_cols_in; i++ ) + if (slx_cols(k) > 0) + slx_control(k,i) = slx_control_in(kk,i); + slx_gear(k) = slx_control_in(kk,1); + slx_isex(k) = slx_control_in(kk,4); + slx_styr(k) = slx_control_in(kk,12); + slx_edyr(k) = slx_control_in(kk,13); + } + nslx_pars = sum(slx_cols); + END_CALCS + + // Load the parameters into their own ragged matrix + matrix slx_par(1,nslx,1,slx_npar); ///> parameter vector for selex parameters + 3darray slx_priors(1,nslx,1,slx_cols,1,3); ///> prior specifications + vector slx_lb(1,nslx_pars); ///> lower bound + vector slx_ub(1,nslx_pars); ///> uppder bound + ivector slx_phzm(1,nslx_pars); ///> phase/mirror + + LOC_CALCS + for ( int k = 1; k <= nslx; k++ ) + { + int kk = slx_indx(k); + for ( int j = 1; j <= slx_cols(k); j++ ) ///> read parameters for each pattern + { + int jj = kk + (j - 1); + slx_par(k,j) = slx_control_in(jj,5); ///> init + slx_priors(k,j,1) = slx_control_in(jj,8); ///> prior type + // If a uniform prior is specified then use the lb and ub rather than p1 and p2 + if ( slx_priors(k,j,1) == UNIFORM_PRIOR ) + { + slx_priors(k,j,2) = slx_control_in(jj,6); ///> p1 + slx_priors(k,j,3) = slx_control_in(jj,7); ///> p2 + } + else + { + slx_priors(k,j,2) = slx_control_in(jj,9); ///> p1 + slx_priors(k,j,3) = slx_control_in(jj,10); ///> p2 + } + slx_lb(jj) = log(slx_control_in(jj,6)); + slx_ub(jj) = log(slx_control_in(jj,7)); + slx_phzm(jj) = slx_control_in(jj,11); + if (slx_type(k) == SELEX_UNIFORM1 || slx_type(k) == SELEX_UNIFORM0) slx_phzm(jj) = -1*abs(slx_phzm(jj)); + } + } + ECHO(slx_priors); + END_CALCS + vector log_slx_pars_init(1,nslx_pars); /// > Initial parameters + + LOC_CALCS + int jj = 1; /// > Set the selectivity parameters + for ( int k = 1; k <= nslx; k++ ) + for ( int i = 1; i <= slx_npar(k); i++ ) + { log_slx_pars_init(jj) = log(slx_par(k,i)); jj++; } + END_CALCS + + init_int NumAsympRet; /// > Number of asymptotic retention + init_matrix AsympSel_control(1,NumAsympRet,1,7); /// > Read the parameters + + ivector AsympSel_fleet(1,NumAsympRet); + ivector AsympSel_sex(1,NumAsympRet); + ivector AsympSel_year(1,NumAsympRet); + vector AsympSel_ival(1,NumAsympRet); + vector AsympSel_lb(1,NumAsympRet); + vector AsympSel_ub(1,NumAsympRet); + ivector AsympSel_phz(1,NumAsympRet); + LOC_CALCS + WriteCtl(NumAsympRet); + if (NumAsympRet > 0) + { + AsympSel_fleet = ivector(column(AsympSel_control,1)); + AsympSel_sex = ivector(column(AsympSel_control,2)); + AsympSel_year = ivector(column(AsympSel_control,3)); + AsympSel_ival = column(AsympSel_control,4); + AsympSel_lb = column(AsympSel_control,5); + AsympSel_ub = column(AsympSel_control,6); + AsympSel_phz = ivector(column(AsympSel_control,7)); + } + WriteCtl(AsympSel_control); + END_CALCS + + // |---------------------------------------------------------| + // | PRIORS FOR CATCHABILITIES OF SURVEYS/INDICES | + // |---------------------------------------------------------| + !! cout << " * Catchability parameter controls" << endl; + init_matrix q_controls(1,nSurveys,1,10); + + vector q_ival(1,nSurveys); + vector q_lb(1,nSurveys); + vector q_ub(1,nSurveys); + ivector q_phz(1,nSurveys); + ivector prior_qtype(1,nSurveys); + vector prior_p1(1,nSurveys); + vector prior_p2(1,nSurveys); + ivector q_anal(1,nSurveys); + vector cpue_lambda(1,nSurveys); + vector cpue_emphasis(1,nSurveys); + LOC_CALCS + WriteCtl(q_controls); + q_ival = column(q_controls,1); + q_lb = column(q_controls,2); + q_ub = column(q_controls,3); + q_phz = ivector(column(q_controls,4)); + prior_qtype = ivector(column(q_controls,5)); + prior_p1 = column(q_controls,6); + prior_p2 = column(q_controls,7); + q_anal = ivector(column(q_controls,8)); + cpue_lambda = column(q_controls,9); + cpue_emphasis = column(q_controls,10); + + for ( int k = 1; k <= nSurveys; k++ ) + { + // If a uniform prior is specified then use the lb and ub rather than p1 and p2. + if ( prior_qtype(k) == UNIFORM_PRIOR ) + { + prior_p1(k) = q_lb(k); + prior_p2(k) = q_ub(k); + } + if ( q_anal(k) == YES ) + { + if ( prior_qtype(k) != UNIFORM_PRIOR || prior_qtype(k) != LOGNORMAL_PRIOR ) + { + cout << "Error: you're only allowed to use a uniform or lognormal prior if the analytic q option is being used," << endl; + cout << " you can either specify a uniform or lognormal prior for q or switch analytic q off." << endl; + exit(1); + } + // If we are using analytic q then turn off estimating this parameter by changing the estimation phase to be -ve. + q_phz(k) = -1; + } + } + ECHO(prior_qtype); ECHO(prior_p1); ECHO(prior_p2); ECHO(cpue_lambda); + END_CALCS + + // |---------------------------------------------------------| + // | ADDITIONAL SURVEY CV CONTROLS | + // |---------------------------------------------------------| + !! cout << " * Additional CV controls" << endl; + init_matrix cv_controls(1,nSurveys,1,7); + !! WriteCtl(cv_controls); + + vector add_cv_ival(1,nSurveys); + vector add_cv_lb(1,nSurveys); + vector add_cv_ub(1,nSurveys); + ivector prior_add_cv_type(1,nSurveys); + vector prior_add_cv_p1(1,nSurveys); + vector prior_add_cv_p2(1,nSurveys); + ivector cv_phz(1,nSurveys); + + vector log_add_cv_ival(1,nSurveys); + vector log_add_cv_lb(1,nSurveys); + vector log_add_cv_ub(1,nSurveys); + + LOC_CALCS + add_cv_ival = column(cv_controls,1); + add_cv_lb = column(cv_controls,2); + add_cv_ub = column(cv_controls,3); + cv_phz = ivector(column(cv_controls,4)); + prior_add_cv_type = ivector(column(cv_controls,5)); + prior_add_cv_p1 = column(cv_controls,6); + prior_add_cv_p2 = column(cv_controls,7); + log_add_cv_ival = log(add_cv_ival); + log_add_cv_lb = log(add_cv_lb); + log_add_cv_ub = log(add_cv_ub); + + for ( int k = 1; k <= nSurveys; k++ ) + { + // If a uniform prior is specified then use the lb and ub rather than p1 and p2. + if ( prior_add_cv_type(k) == UNIFORM_PRIOR ) + { + prior_add_cv_p1(k) = add_cv_lb(k); + prior_add_cv_p2(k) = add_cv_ub(k); + } + } + ECHO(prior_add_cv_type); ECHO(prior_add_cv_p1); ECHO(prior_add_cv_p2); + END_CALCS + + // |---------------------------------------------------------| + // | PENALTIES FOR MEAN FISHING MORTALITY RATE FOR EACH GEAR | + // |---------------------------------------------------------| + !! cout << " * Fishing mortality controls" << endl; + init_matrix f_controls(1,nfleet,1,6); + + ivector f_phz(1,nfleet); + ivector foff_phz(1,nfleet); + vector pen_fbar(1,nfleet); + vector log_pen_fbar(1,nfleet); + vector log_pen_fbar_foff(1,nfleet); + matrix pen_fstd(1,2,1,nfleet); + LOC_CALCS + WriteCtl(f_controls); + pen_fbar = column(f_controls,1); + log_pen_fbar = log(pen_fbar + 1.0e-14); + log_pen_fbar_foff = column(f_controls,2); + log_pen_fbar_foff = log(log_pen_fbar_foff + 1.0e-14); + for ( int i = 1; i <= 2; i++ ) pen_fstd(i) = trans(f_controls)(i+2); + f_phz = ivector(column(f_controls,5)); + foff_phz = ivector(column(f_controls,6)); + ECHO(f_phz); + ECHO(foff_phz); + END_CALCS + + // |-----------------------------------| + // | OPTIONS FOR SIZE COMPOSITION DATA | + // |-----------------------------------| + !! cout << " * Size composition controls" << endl; + init_ivector nAgeCompType_in(1,nSizeComps_in); ///> Size-comp likelihood + !! for (int k =1; k<=nSizeComps_in; k++) + !! if (nAgeCompType_in(k) != 0 & nAgeCompType_in(k) != 1 & nAgeCompType_in(k) != 2 & nAgeCompType_in(k) != 5) + !! { cout << "Size comp type must be 0, 1, 2 or 5" << endl; exit(1); } + init_ivector bTailCompression_in(1,nSizeComps_in); ///> option for tail compression + init_vector nvn_ival_in(1,nSizeComps_in); ///> initial value for effective sample size + init_ivector nvn_phz_in(1,nSizeComps_in); ///> Phase for effective sample size + init_ivector iCompAggregator(1,nSizeComps_in); ///> shoul data be aggregated + init_vector lf_lambda_in(1,nSizeComps_in); ///> Lambda for effect N + init_vector lf_emphasis_in(1,nSizeComps_in); ///> Overall lambda + + int nSizeComps; + !! nSizeComps = max(iCompAggregator); ///> Number of length comps after compression + ivector nSizeCompRows(1,nSizeComps); + ivector nSizeCompCols(1,nSizeComps); + ivector nAgeCompType(1,nSizeComps); + ivector bTailCompression(1,nSizeComps); + vector log_nvn_ival(1,nSizeComps); + ivector nvn_phz(1,nSizeComps); + vector lf_lambda(1,nSizeComps); + vector lf_emphasis(1,nSizeComps); + + LOC_CALCS + WriteCtl(nAgeCompType_in); + WriteCtl(bTailCompression_in); + WriteCtl(nvn_ival_in); + WriteCtl(nvn_phz_in); + WriteCtl(iCompAggregator); + WriteCtl(lf_lambda_in); + WriteCtl(lf_emphasis_in); + nSizeCompCols.initialize(); + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + // Currently this only works if the number of rows in each size composition group are the same and have the same years. If not then gmacs throws an error. + nSizeCompRows(k) = nSizeCompRows_in(kk); + // We are appending the arrays horizontally. + nSizeCompCols(k) += nSizeCompCols_in(kk); + // Again, we are using only the last specification here, may want to add a check to ensure the user specifies that these are the same and throw an error if not. + nAgeCompType(k) = nAgeCompType_in(kk); + bTailCompression(k) = bTailCompression_in(kk); + log_nvn_ival(k) = log(nvn_ival_in(kk)); + nvn_phz(k) = nvn_phz_in(kk); + lf_lambda(k) = lf_lambda_in(kk); + lf_emphasis(k) = lf_emphasis_in(kk); + } + // Do the checks mentioned above + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + if ( nSizeCompRows(k) != nSizeCompRows_in(kk) ) + { + cout << "Error: dimension mismatch in size-compositons being aggregated" << endl; + exit(1); + } + if ( nAgeCompType(k) != nAgeCompType_in(kk) ) + { + cout << "Error: mismatch in type of likelihood for size-compositons being aggregated" << endl; + exit(1); + } + if ( bTailCompression(k) != bTailCompression_in(kk) ) + { + cout << "Error: mismatch in auto tail compression for size-compositons being aggregated" << endl; + exit(1); + } + if ( log_nvn_ival(k) != log(nvn_ival_in(kk)) ) + { + cout << "Error: mismatch in initial value of effctive sample size for size-compositons being aggregated" << endl; + exit(1); + } + if ( nvn_phz(k) != nvn_phz_in(kk) ) + { + cout << "Error: mismatch in phase for estimation of effctive sample size for size-compositons being aggregated" << endl; + exit(1); + } + } + END_CALCS + + 3darray d3_SizeComps(1,nSizeComps,1,nSizeCompRows,-7,nSizeCompCols); + 3darray d3_obs_size_comps(1,nSizeComps,1,nSizeCompRows,1,nSizeCompCols); + matrix size_comp_sample_size(1,nSizeComps,1,nSizeCompRows); + matrix size_comp_year(1,nSizeComps,1,nSizeCompRows); + ivector ilike_vector(1,nlikes) + + LOC_CALCS + int i,j; + int oldk = 9999; + // This aggregates the size composition data by appending size comps horizontally + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + if ( oldk != k ) j = 0; + oldk = k; + for ( int jj = 1; jj <= nSizeCompCols_in(kk); jj++ ) + { + j += 1; + for ( int ii = 1; ii <= nSizeCompRows_in(kk); ii++ ) + { i = ii; d3_obs_size_comps(k,i,j) = d3_obs_size_comps_in(kk,ii,jj); } + } + } + // The size composition sample sizes are calculated as the sum of the aggregated sample sizes + size_comp_sample_size.initialize(); + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + size_comp_year(k) = size_comp_year_in(kk); + for ( int ii = 1; ii <= nSizeCompRows_in(kk); ii++ ) + size_comp_sample_size(k,ii) += size_comp_sample_size_in(kk,ii); + } + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + for ( int ii = 1; ii <= nSizeCompRows_in(kk); ii++ ) + { + if ( size_comp_year(k,ii) != size_comp_year_in(kk,ii) ) + { + cout << "Error: mismatch in years for size-compositons being aggregated" << endl; + cout << " see the " << size_comp_year_in(kk,ii) << " year in size composition " << kk << " in the .dat file" << endl; + exit(1); + } + } + } + // This normalizes all observations by row + for ( int k = 1; k <= nSizeComps; k++ ) + for ( int i = 1; i <= nSizeCompRows(k); i++ ) + d3_obs_size_comps(k,i) /= sum(d3_obs_size_comps(k,i)); + + ECHO(d3_obs_size_comps); + ilike_vector(1) = nCatchDF; + like_names = adstring("Catch "); + ilike_vector(2) = nSurveys; + like_names = like_names + adstring("Survey_Indices "); + ilike_vector(3) = nSizeComps; + like_names = like_names + adstring("SizeComps "); + ilike_vector(4) = 3; + ilike_vector(5) = nsex; + END_CALCS + + // |-------------------------------------| + // | OPTIONS FOR NATURAL MORTALITY RATES | + // |-------------------------------------| + !! cout << " * Natural mortality controls" << endl; + int nMdev; + init_int m_type; + int MrelFem; + !! if (nsex>1) + !! { + !! *(ad_comm::global_datafile) >> MrelFem; + !! if (MrelFem != YES & MrelFem != NO) + !! { cout << "MrelFem can only be 0[No; absolute] or 1[Relative]; STOPPING" << endl; exit(1); } + !! } + + init_int Mdev_phz_def; + init_number m_stdev; + init_ivector m_nNodes_sex(1,nsex) + init_imatrix m_nodeyear_sex(1,nsex,1,m_nNodes_sex); + init_int nSizeDevs; + init_ivector m_size_nodeyear(1,nSizeDevs); + init_int Init_Mdev; + ivector nMdev_par_cnt(1,nsex); + + LOC_CALCS + WriteCtl(m_type); + if (nsex>1) WriteCtl(MrelFem); + WriteCtl(Mdev_phz_def); + WriteCtl(m_stdev); + WriteCtl(m_nNodes_sex); + WriteCtl(m_nodeyear_sex); + WriteCtl(nSizeDevs); + WriteCtl(m_size_nodeyear); + WriteCtl(Init_Mdev); + + switch ( m_type ) + { + nMdev_par_cnt.initialize(); + case M_CONSTANT: + nMdev = 0; + Mdev_phz_def = -1; + break; + case M_RANDOM: + nMdev = nsex*(nyr - syr); + nMdev_par_cnt(1) = (nyr - syr); + if (nsex > 0) nMdev_par_cnt(2) = (nyr - syr); + break; + case M_CUBIC_SPLINE: + nMdev = sum(m_nNodes_sex); + nMdev_par_cnt(1) = m_nNodes_sex(1); + if (nsex>1) nMdev_par_cnt(2) = m_nNodes_sex(2); + break; + case M_BLOCKED_CHANGES: + nMdev = sum(m_nNodes_sex); + nMdev_par_cnt(1) = m_nNodes_sex(1); + if (nsex>1) nMdev_par_cnt(2) = m_nNodes_sex(2); + break; + case M_TIME_BLOCKS1: //added by Jie Zheng + nMdev = sum(m_nNodes_sex)/2; + nMdev_par_cnt(1) = m_nNodes_sex(1)/2; + if (nsex>1) nMdev_par_cnt(2) = m_nNodes_sex(2)/2; + break; + case M_TIME_BLOCKS2: + nMdev = sum(m_nNodes_sex); + nMdev_par_cnt(1) = m_nNodes_sex(1); + if (nsex>1) nMdev_par_cnt(2) = m_nNodes_sex(2); + break; + } + END_CALCS + !! nMdev += nSizeDevs; + vector Mdev_ival(1,nMdev); + vector Mdev_lb(1,nMdev); + vector Mdev_ub(1,nMdev); + ivector Mdev_phz(1,nMdev); + ivector Mdev_spec(1,nMdev); + LOC_CALCS + if (Init_Mdev==YES) + { + for (int I = 1; I <=nMdev; I++) + { + *(ad_comm::global_datafile) >> Mdev_ival(I) >> Mdev_lb(I)>> Mdev_ub(I) >> Mdev_phz(I) >> Mdev_spec(I); + gmacs_ctl << "# " << "#Mparameters" << "\n" << " " << Mdev_ival(I) << " " << Mdev_lb(I) << " " << Mdev_ub(I) << " " << Mdev_phz(I) << " " << Mdev_spec(I) << endl; + if (Mdev_spec(I) < 0) Mdev_phz(I) = -1; + } + } + else + { + for (int I = 1; I <=nMdev; I++) + { Mdev_ival(I) = 0; Mdev_lb(I) = -3; Mdev_ub(I) = 3; Mdev_phz(I) = Mdev_phz_def; } + } + END_CALCS + + // |---------------------------------------------------------| + // | OTHER CONTROLS | + // |---------------------------------------------------------| + !! cout << " * Other controls" << endl; + init_vector model_controls(1,10); + int rdv_syr; ///> First year of estimates devs + int rdv_eyr; ///> First year of estimates devs + int rdv_phz; ///> Estimated rec_dev phase + int rec_ini_phz; ///> Estimated rec_ini phase + int verbose; ///> Flag to print to screen + int StopAfterFnCall ///> Number of function calls to stop after + + int bInitializeUnfished; ///> Flag to initialize at unfished conditions + int bSteadyState; ///> Variable to store option related to initial state + number spr_lambda; ///> Oroportion of mature male biomass for MMB + int nSRR_flag; ///> if nSRR_flag == 1 then use a Beverton-Holt model to compute the recruitment deviations for minimization. + int rec_prop_phz; ///> Phase for sex ratios + int TurnOffPhase; ///> Maximum phase + int nthetatest; ///> Checking that the number of thetas is correct + LOC_CALCS + rdv_syr = int(model_controls(1)); + rdv_eyr = int(model_controls(2)); + rdv_phz = int(model_controls(3)); + if (nsex==1) rec_prop_phz = -1; else rec_prop_phz = rdv_phz; + + rec_ini_phz = int(model_controls(4)); + verbose = int(model_controls(5)); + bInitializeUnfished = int(model_controls(6)); + spr_lambda = model_controls(7); + nSRR_flag = int(model_controls(8)); + TurnOffPhase = int(model_controls(9)); + StopAfterFnCall = int(model_controls(10)); + WriteCtl(model_controls); + + // Some combinations of options don't work + if ( bInitializeUnfished == UNFISHEDEQN & theta_phz(nsex+2) > 0 ) + { cout << "Error: cannot LogRini when unfished steady state is selected" << endl; exit(1); } + if ( bInitializeUnfished == FISHEDEQN & theta_phz(nsex+1) > 0 ) + { cout << "Error: cannot LogR0 when fished steady state is selected" << endl; exit(1); } + if ( bInitializeUnfished == FREEPARS && theta_phz(nsex+1) > 0 ) + { + cout << "Error: cannot estimate LogR0 when inidvidual parameters are estimated" << endl; + exit(1); + } + if ( bInitializeUnfished == FREEPARS && theta_phz(nsex+2) > 0 ) + { + cout << "Error: cannot estimate LogRini when inidvidual parameters are estimated" << endl; + exit(1); + } + if ( (bInitializeUnfished == FREEPARS || bInitializeUnfished == FREEPARSSCALED) && rec_ini_phz > 0 ) + { + cout << "Error: cannot estimate initial rec_devs for free parameters" << endl; + exit(1); + } + + // Default number of parameters + nthetatest = nsex+3 + 2*nsex + 3; + if (bInitializeUnfished == FISHEDEQN) nthetatest += nfleet; + if (bInitializeUnfished == FREEPARS) nthetatest += nclass*nsex*nmature*nshell; + if (bInitializeUnfished == FREEPARSSCALED) nthetatest += (nclass*nsex*nmature*nshell-1); + if (nthetatest != ntheta) + { + cout << "Error: the specified number of theta parameters does not match the expected value of " << nthetatest << endl; + exit(1); + } + END_CALCS + init_vector catch_emphasis(1,nCatchDF); ///> Weights on catches + !! WriteCtl(catch_emphasis); + init_vector Penalty_emphasis(1,7); ///> Weights on penalties + !! WriteCtl(Penalty_emphasis); + + init_int eof_ctl; + !! WriteCtl(eof_ctl); + !! if ( eof_ctl != 9999 ){cout << "Error reading control file" << endl; exit(1);} + !! cout << "end of control section" << endl; + +// ================================================================================================ + + !! ad_comm::change_datafile_name(projectfile); + !! cout << "+-------------------------+" << endl; + !! cout << "| Reading projection file |" << endl; + !! cout << "+-------------------------+" << endl; + init_int Calc_MSY; + !! WRITEPRJ(Calc_MSY); + !! if (Calc_MSY != NO && Calc_MSY != YES) { cout << "Indicate 1=Yes or 0=No for whether MSY should be computed" << endl; exit(1); } + init_ivector Ffixed(1,nfleet); + !! WRITEPRJ(Ffixed); + !! for (int k=1;k<=nfleet;k++) + !! if (Ffixed(k) != NO & Ffixed(k) != YES) { cout << "Future F indicator must be 0 or 1" << endl; exit(1); } + init_int spr_syr; + !! WRITEPRJ(spr_syr); + !! if (spr_syr < syr) { cout << "First year for computing Rbar must be syr or later: STOPPING" << endl; exit(1); } + init_int spr_nyr; + !! WRITEPRJ(spr_nyr); + !! if (spr_nyr > nyr) { cout << "Last year for computing Rbar must be nyr or earlier: STOPPING" << endl; exit(1); } + !! if( spr_nyr < spr_syr) { cout << "Last year for computing Rbar must be later than the first year" << endl; exit(1); } + + init_int spr_aveF_syr; + !! WRITEPRJ(spr_aveF_syr); + !! if (spr_aveF_syr < syr) { cout << "First year for computing AveF must be syr or later: STOPPING" << endl; exit(1); } + init_int spr_aveF_nyr; + !! WRITEPRJ(spr_aveF_nyr); + !! if (spr_aveF_nyr > nyr) { cout << "Last year for computing AveF must be nyr or earlier: STOPPING" << endl; exit(1); } + !! if( spr_aveF_nyr < spr_aveF_syr) { cout << "Last year for computing AveF must be later than the first year" << endl; exit(1); } + + init_number spr_target; + !! WRITEPRJ(spr_target); + init_int OFLTier; + !! WRITEPRJ(OFLTier); + init_number OFLalpha; + !! WRITEPRJ(OFLalpha); + init_number OFLbeta; + !! WRITEPRJ(OFLbeta); + init_number OFLgamma; + !! WRITEPRJ(OFLgamma); + init_number ABCBuffer; + !! WRITEPRJ(ABCBuffer); + init_int Compute_yield_prj; + !! if (Compute_yield_prj != NO & Compute_yield_prj != YES) { cout << "Indicate 1=Yes or 0=No for whether the yield function should be reported" << endl; exit(1); } + !! if (Calc_MSY==NO) Compute_yield_prj = NO; + !! WRITEPRJ(Compute_yield_prj); + + int Eqn_basis; ///> Option for recruitment (0=Constant; 1+ is SRR) + + int nproj; ///> number of projection years + init_int pyr; ///> terminal projection year + !! WRITEPRJ(pyr); + init_int prj_Nstrat; ///> Account for state stragey + !! WRITEPRJ(prj_Nstrat); + !! nproj = pyr - nyr; + !! if (nproj < 0) { cout << "Terminal year for project must be later tha nyr: STOPPING" << endl; exit(1); } + init_number prj_lowF; ///> Low F for projections + !! WRITEPRJ(prj_lowF); + init_number prj_hiF; ///> High F for projections + !! WRITEPRJ(prj_hiF); + init_int prj_bycatch_on; ///> Allow for bycatch fleets to have non-zero mortality + !! WRITEPRJ(prj_bycatch_on); + init_int prj_replicates; ///> How many times each MCMC draw is run + !! WRITEPRJ(prj_replicates); + init_number Fixed_prj_Bmsy; ///> Should Bmsy be fixed (negative numbers) + !! WRITEPRJ(Fixed_prj_Bmsy); + + init_int Stock_rec_prj; ///> Stock-recruitment relationship + !! WRITEPRJ(Stock_rec_prj); + init_int Age_at_rec_prj; ///> Age-at-recruitment + !! WRITEPRJ(Age_at_rec_prj); + init_int prj_futRec_syr; + !! WRITEPRJ(prj_futRec_syr); + !! if (prj_futRec_syr < syr) { cout << "First year for generating recruitment must be syr or later: STOPPING" << endl; exit(1); } + init_int prj_futRec_nyr; + !! WRITEPRJ(prj_futRec_nyr); + !! if (prj_futRec_nyr > nyr) { cout << "Last year for generating recruitment must be nyr or earlier: STOPPING" << endl; exit(1); } + !! if(prj_futRec_nyr < prj_futRec_syr) { cout << "Last year for generating recruitment must be later than the first year" << endl; exit(1); } + init_number SigmaR_prj ///> Sigma(R) + !! WRITEPRJ(SigmaR_prj); + + init_int Apply_HCR_prj; ///> State HCR stuff + !! WRITEPRJ(Apply_HCR_prj); + init_number MeanWStateMature; + !! WRITEPRJ(MeanWStateMature); + init_number MeanWStateLegal; + !! WRITEPRJ(MeanWStateLegal); + + init_int max_prj; + !! WRITEPRJ(max_prj); + + init_int eof_prj; + !! WRITEPRJ(eof_prj); + !! if ( eof_prj != 9999 ){cout << "Error reading projection file: " << eof_prj << endl; exit(1);} + !! cout << "end of projection section" << endl; + +// ================================================================================================ + + int IsProject; ///> Are we in projection mode + + // special constants (used for guassan quadrature) + vector xg(1,32) + vector wg(1,32) + 3darray l1_vec(1,nsex,1,nclass,1,32) ///> temp storage + + // Phases off + int NVarPar; + LOC_CALCS + NVarPar = 0; + int Ipar; + for (Ipar=1;Ipar<=ntheta; Ipar++) if (theta_phz(Ipar) > TurnOffPhase) theta_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nGrwth; Ipar++) if (Grwth_phz(Ipar) > TurnOffPhase) Grwth_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nslx_pars; Ipar++) if (slx_phzm(Ipar) > TurnOffPhase) slx_phzm(Ipar) = -1; + for (Ipar=1;Ipar<=NumAsympRet; Ipar++) if (AsympSel_phz(Ipar) > TurnOffPhase) AsympSel_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nfleet; Ipar++) if (f_phz(Ipar) > TurnOffPhase) f_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nfleet; Ipar++) if (foff_phz(Ipar) > TurnOffPhase) foff_phz(Ipar) = -1; + if (rec_ini_phz > TurnOffPhase) rec_ini_phz = -1; + if (rdv_phz > TurnOffPhase) rdv_phz = -1; + if (rec_prop_phz > TurnOffPhase) rec_prop_phz = -1; + for (Ipar=1;Ipar<=nMdev; Ipar++) if (Mdev_phz(Ipar) > TurnOffPhase) Mdev_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nSizeComps; Ipar++) if (nvn_phz(Ipar) > TurnOffPhase) nvn_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nSurveys; Ipar++) if (q_phz(Ipar) > TurnOffPhase) q_phz(Ipar) = -1; + for (Ipar=1;Ipar<=nSurveys; Ipar++) if (cv_phz(Ipar) > TurnOffPhase) cv_phz(Ipar) = -1; + + for (Ipar=1;Ipar<=ntheta; Ipar++) if (theta_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nGrwth; Ipar++) if (Grwth_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nslx_pars; Ipar++) if (slx_phzm(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=NumAsympRet; Ipar++) if (AsympSel_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nfleet; Ipar++) if (f_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nfleet; Ipar++) if (f_phz(Ipar) > 0) NVarPar += nFparams(Ipar); + for (Ipar=1;Ipar<=nfleet; Ipar++) if (foff_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nfleet; Ipar++) if (foff_phz(Ipar) > 0) NVarPar += nYparams(Ipar); + if (rec_ini_phz > 0) NVarPar += nclass; + if (rdv_phz > 0) NVarPar += (rdv_eyr-rdv_syr+1); + if (rec_prop_phz > 0) NVarPar += (rdv_eyr-rdv_syr+1); + for (Ipar=1;Ipar<=nMdev; Ipar++) if (Mdev_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nSizeComps; Ipar++) if (nvn_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nSurveys; Ipar++) if (q_phz(Ipar) > 0) NVarPar += 1; + for (Ipar=1;Ipar<=nSurveys; Ipar++) if (cv_phz(Ipar) > 0) NVarPar += 1; + END_CALCS + + int PhaseGrowthPar; + int PhaseSelexPar; + LOC_CALCS + PhaseGrowthPar = 10000; ///> Lowest phase for a growth parameter + for (Ipar=1;Ipar<=nGrwth; Ipar++) if (Grwth_phz(Ipar) > 0 & Grwth_phz(Ipar) < PhaseGrowthPar) PhaseGrowthPar = Grwth_phz(Ipar); + PhaseSelexPar = 10000; + for (Ipar=1;Ipar<=nslx; Ipar++) if (slx_phzm(Ipar) > 0 & slx_phzm(Ipar) < PhaseSelexPar) PhaseSelexPar = slx_phzm(Ipar); + for (Ipar=1;Ipar<=NumAsympRet; Ipar++) if (AsympSel_phz(Ipar) > 0 & AsympSel_phz(Ipar) < PhaseSelexPar) PhaseSelexPar = AsympSel_phz(Ipar); + END_CALCS + + int NfunCall; + !! NfunCall = 0; + +// ================================================================================================ + +INITIALIZATION_SECTION + theta theta_ival; + Grwth Grwth_ival; + Asymret AsympSel_ival + log_fbar log_pen_fbar; + log_vn log_nvn_ival; + survey_q q_ival; + logit_rec_prop_est 0.0; + log_add_cv log_add_cv_ival; + m_dev_est Mdev_ival; + log_slx_pars log_slx_pars_init + +// ================================================================================================ + +PARAMETER_SECTION + !! cout << "+----------------------+" << endl; + !! cout << "| Parameter section |" << endl; + !! cout << "+----------------------+" << endl; + // Leading parameters + // M = theta(1) + // ln(Ro) = theta(2) + // ln(R1) = theta(3) + // ln(Rbar) = theta(4) + // ra = theta(5) + // rbeta = theta(6) + // ra = theta(7) + // rbeta = theta(8) + // logSigmaR = theta(9+) + // steepness = theta(10) + // rho = theta(11) + // logN0 = theta(12,...) + init_bounded_number_vector theta(1,ntheta,theta_lb,theta_ub,theta_phz); + !! ECHO(theta); + // Growth and molting probability parameters (sex-specific) + init_bounded_number_vector Grwth(1,nGrwth,Grwth_lb,Grwth_ub,Grwth_phz); + !!ECHO(Grwth); + // Selectivity parameters + init_bounded_number_vector log_slx_pars(1,nslx_pars,slx_lb,slx_ub,slx_phzm); + !!ECHO(log_slx_pars); + // Asymptotic retention + init_bounded_number_vector Asymret(1,NumAsympRet,AsympSel_lb,AsympSel_ub,AsympSel_phz); + !!ECHO(Asymret); + + // Fishing mortality rate parameters + init_bounded_number_vector log_fbar(1,nfleet,-10,2,f_phz); ///> Male mean fishing mortality + !! ECHO(f_phz); + !! ECHO(log_fbar); + init_bounded_vector_vector log_fdev(1,nfleet,1,nFparams,-10,10,f_phz); ///> Male f devs + !! ECHO(nFparams); + !! for (int I=1;I<=nfleet;I++) { ECHO(log_fdev(I)); } + init_bounded_number_vector log_foff(1,nfleet,-10,6,foff_phz); ///> Female F offset to Male F + !! ECHO(log_foff); + !! ECHO(foff_phz); + init_bounded_vector_vector log_fdov(1,nfleet,1,nYparams,-10,10,foff_phz); ///> Female F offset to Male F + !!ECHO(nYparams); + !! for (int I=1;I<=nfleet;I++) { ECHO(log_fdov(I)); } + + // Recruitment deviation parameters + init_bounded_dev_vector rec_ini(1,nclass,-14.0,14.0,rec_ini_phz); ///> initial size devs + !! ECHO(rec_ini); + init_bounded_dev_vector rec_dev_est(rdv_syr,rdv_eyr,-8.0,8.0,rdv_phz); ///> recruitment deviations + !! ECHO(rec_dev_est); + vector rec_dev(syr,nyr); + init_bounded_dev_vector logit_rec_prop_est(rdv_syr,rdv_eyr,-100,100,rec_prop_phz); ///> recruitment deviations + !! ECHO(logit_rec_prop_est); + vector logit_rec_prop(syr,nyr); + + // Time-varying natural mortality rate devs. + init_bounded_number_vector m_dev_est(1,nMdev,Mdev_lb,Mdev_ub,Mdev_phz); ///> natural mortality deviations + !! ECHO(nMdev); + vector m_dev(1,nMdev); + matrix m_dev_sex(1,nsex,1,nMdev_par_cnt); ///> natural mortality deviations + vector Msize(1,nclass); + + // Effective sample size parameter for multinomial + init_number_vector log_vn(1,nSizeComps,nvn_phz); + !! ECHO(log_vn); + + // Catchability coefficient (q) + init_bounded_number_vector survey_q(1,nSurveys,q_lb,q_ub,q_phz); + !! ECHO(survey_q); + + // Addtional CV for surveys/indices + init_bounded_number_vector log_add_cv(1,nSurveys,log_add_cv_lb,log_add_cv_ub,cv_phz); + !! ECHO(log_add_cv); + +// -------------------------------------------------------------------------------------------------- + + // Items related to the objective function + vector priorDensity(1,NVarPar); + matrix nloglike(1,nlikes,1,ilike_vector); + vector nlogPenalty(1,7); + matrix sdnr_MAR_cpue(1,nSurveys,1,2); + matrix sdnr_MAR_lf(1,nSizeComps,1,2); + vector Francis_weights(1,nSizeComps); + + objective_function_value objfun; + + vector M0(1,nsex); ///> natural mortality rate + number logR0; ///> logarithm of unfished recruits + number logRbar; ///> logarithm of average recruits(syr+1,nyr) + number logRini; ///> logarithm of initial recruitment(syr) + vector ra(1,nsex); ///> Expected value of recruitment distribution + vector rbeta(1,nsex); ///> rate parameter for recruitment distribution + number logSigmaR; ///> standard deviation of recruitment deviations + number steepness; ///> steepness of the SRR + number rho; ///> autocorrelation coefficient in recruitment + matrix logN0(1,n_grp,1,nclass); ///> initial numbers at length + + vector alpha(1,nsex); ///> intercept for linear growth increment model + vector beta(1,nsex); ///> slope for the linear growth increment model + matrix rec_sdd(1,nsex,1,nclass); ///> recruitment size_density_distribution + matrix gscale(1,nsex,1,maxSizeIncVaries); ///> scale parameter for the gamma distribution + matrix molt_mu(1,nsex,1,nMoltVaries); ///> 50% probability of molting at length each year + matrix molt_cv(1,nsex,1,nMoltVaries); ///> CV in molting probabilility + matrix Linf(1,nsex,1,maxSizeIncVaries); ///> Mean Linf + matrix Kappa(1,nsex,1,maxSizeIncVaries); ///> Mean Kappa + matrix SigmaKappa(1,nsex,1,maxSizeIncVaries); ///> SD of kappa + matrix SigmaLinf(1,nsex,1,maxSizeIncVaries); ///> SD of linf + + matrix recruits(1,nsex,syr,nyr); ///> vector of estimated recruits + vector res_recruit(syr,nyr); ///> vector of estimated recruits + vector xi(syr,nyr); ///> vector of residuals for SRR + + matrix pre_catch(1,nCatchDF,1,nCatchRows); ///> predicted catch (Baranov eq) + matrix res_catch(1,nCatchDF,1,nCatchRows); ///> catch residuals in log-space + matrix obs_catch_effort(1,nCatchDF,1,nCatchRows); ///> inferred catch if there is no catch but some effort + matrix pre_catch_out(1,nCatchDF,syr,nyr); ///> Predicted catch for output + matrix res_catch_out(1,nCatchDF,syr,nyr); ///> Residuals for output + vector log_q_catch(1,nCatchDF); ///> Catchability + + 3darray molt_increment(1,nsex,1,maxSizeIncVaries,1,nclass); ///> linear molt increment + 3darray molt_probability(1,nsex,syr,nyr,1,nclass); ///> probability of molting + 3darray ProbMolt(1,nsex,1,nclass,1,nclass); ///> Diagonal matrix of molt probabilities + 4darray growth_transition(1,nsex,1,maxSizeIncVaries,1,nclass,1,nclass); ///> The time-dependent growth transition matrix + + 4darray log_slx_capture(1,nfleet,1,nsex,syr,nyr+1,1,nclass); ///> capture selectivity + 4darray log_slx_retaind(1,nfleet,1,nsex,syr,nyr+1,1,nclass); ///> probability of retention + 4darray log_slx_discard(1,nfleet,1,nsex,syr,nyr+1,1,nclass); ///> probabilty of disards + 3darray log_high_grade(1,nfleet,1,nsex,syr,nyr+1); ///> high-grading fraction + + 3darray M(1,nsex,syr,nyr,1,nclass); ///> Natural mortality + vector Mmult(1,nclass); ///> size-class-specific multiplier + matrix fout(1,nfleet,syr,nyr); ///> Fishing mortality output + vector finit(1,nfleet); ///> Initial F + 4darray ft(1,nfleet,1,nsex,syr,nyr,1,nseason); ///> Fully-selected fishing mortality by gear + 4darray F(1,nsex,syr,nyr,1,nseason,1,nclass); ///> Fishing mortality actual + 4darray F2(1,nsex,syr,nyr,1,nseason,1,nclass); ///> Fishing mortality with full selection + 4darray Z(1,nsex,syr,nyr,1,nseason,1,nclass); ///> Total mortality actual + 4darray Z2(1,nsex,syr,nyr,1,nseason,1,nclass); ///> Total mortality with full selection + 5darray S(1,nsex,syr,nyr,1,nseason,1,nclass,1,nclass); ///> Surival Rate (S=exp(-Z)) + + 4darray d4_N(1,n_grp,syr,nyr+1,1,nseason,1,nclass); ///> Numbers-at-sex/mature/shell/year/season/length. + 3darray d3_newShell(1,nsex,syr,nyr+1,1,nclass); ///> New shell crabs-at-length. + 3darray d3_oldShell(1,nsex,syr,nyr+1,1,nclass); ///> Old shell crabs-at-length. + + number TempSS; ///> Use to compute the selextivity penalty + + matrix pre_cpue(1,nSurveys,1,nSurveyRows); ///> predicted relative abundance index + matrix res_cpue(1,nSurveys,1,nSurveyRows); ///> relative abundance residuals + + // Observed and predicted catch-at-size and results + 3darray d3_pre_size_comps_in(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); + 3darray d3_res_size_comps_in(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); + 3darray d3_obs_size_comps_out(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); // _out is for output/plotting purposes + 3darray d3_pre_size_comps_out(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); // _out is for output/plotting purposes + 3darray d3_res_size_comps_out(1,nSizeComps_in,1,nSizeCompRows_in,1,nSizeCompCols_in); // _out is for output/plotting purposes + 3darray d3_pre_size_comps(1,nSizeComps,1,nSizeCompRows,1,nSizeCompCols); + 3darray d3_res_size_comps(1,nSizeComps,1,nSizeCompRows,1,nSizeCompCols); + + // Use to compute the likelihood of the tagging data + 5darray FullY(1,nsex,1,maxSizeIncVaries,1,MaxGrowTimeLib,1,nclass,1,nclass); + + // Passing variable + number ssb_pass; ///> SSB passed back + number ofltot_pass; ///> Retained OFL passed back + number oflret_pass; ///> Total OFL passed back + vector log_fimpbar(1,nfleet); ///> Vector of Fs + vector log_fimpbarOFL(1,nfleet); ///> Vector of Fs for computing OFL + vector log_fimpbarPass(1,nfleet); ///> Vector of Fs + vector catch_pass(1,2+nfleet); ///> Various catch outputs + + // SPR-related stuff + number spr_rbar; ///> Mean recruitment for SPR calculations + number ssbF0; ///> Unfished MMB + number spr_bmsy; ///> MMB corresponding to FMSY + number spr_depl; ///> MMB relative to BMSY for the OFL + number spr_cofl; ///> OFL + number spr_fofl; ///> F relative to FMSY for the OFL + number Bmsy; ///> Also MMB corresponding to FMSY + + // Projection stuff + number SR_alpha_prj; ///> Alpha for projections + number SR_beta_prj; ///> Beta for projections + number Steepness; ///> Stock-recruitment steepness + matrix spr_yield(0,100,1,4); ///> Yield function + matrix fut_recruits(1,nsex,1,nproj); ///> Projected recruitment + + // Extra sd variables + vector sd_fbar(syr,nyr-1); + vector sd_log_dyn_Bzero(syr+1,nyr); + sdreport_number sd_rbar; + sdreport_number sd_ssbF0; + sdreport_number sd_Bmsy; + sdreport_number sd_depl; + sdreport_vector sd_fmsy(1,nfleet); + sdreport_vector sd_fofl(1,nfleet); + sdreport_number sd_ofl; + sdreport_matrix sd_log_recruits(1,nsex,syr,nyr); + sdreport_vector sd_log_ssb(syr,nyr); + sdreport_vector ParsOut(1,NVarPar); + //added nine lines by Jie + sdreport_number sd_last_ssb; + sdreport_vector sdrLnRecMMB(syr,nyr-7); //these are for spawning per recruits. Six years of recruitment time lag. + sdreport_vector sdrLnRec(syr,nyr-7); + sdreport_vector sdrRec(syr,nyr-7); + sdreport_vector sdrMMB(syr,nyr-7); + //sdreport_vector sdrLnRecMMB(syr+1,nyr); //these are for recruits. + //sdreport_vector sdrLnRec(syr+1,nyr); + //sdreport_vector sdrRec(syr+1,nyr); + //sdreport_vector sdrMMB(syr+1,nyr); + + // sdreport_vector sd_fbar(syr,nyr-1); + // sdreport_vector sd_log_dyn_Bzero(syr+1,nyr); + + friend_class population_model; + +// ================================================================================================ + +PRELIMINARY_CALCS_SECTION + cout << "+----------------------+" << endl; + cout << "| Preliminary section |" << endl; + cout << "+----------------------+" << endl; + + // 32 Gaussian evaluation points + xg( 1)=-0.99726; xg( 2)=-0.98561; xg( 3)=-0.96476; xg( 4)=-0.93490; xg( 5)=-0.89632; xg( 6)=-0.84936; xg( 7)=-0.79448; xg( 8)=-0.73218; + xg( 9)=-0.66304; xg(10)=-0.58771; xg(11)=-0.50689; xg(12)=-0.42135; xg(13)=-0.33186; xg(14)=-0.23928; xg(15)=-0.14447; xg(16)= -0.0483; + xg(17)= 0.04830; xg(18)= 0.14447; xg(19)= 0.23928; xg(20)= 0.33186; xg(21)= 0.42135 ;xg(22)= 0.50689; xg(23)= 0.58771; xg(24)= 0.66304; + xg(25)= 0.73218; xg(26)= 0.79448; xg(27)= 0.84936; xg(28)= 0.89632; xg(29)= 0.93490; xg(30)= 0.96476; xg(31)= 0.98561; xg(32)= 0.99726; + + // 32 Gaussian weights + wg( 1)=0.00701; wg( 2)=0.01627; wg( 3)=0.02539; wg( 4)=0.03427; wg( 5)=0.04283; wg( 6)=0.05099; wg( 7)=0.05868; wg( 8)=0.06582; + wg( 9)=0.07234; wg(10)=0.07819; wg(11)=0.08331; wg(12)=0.08765; wg(13)=0.09117; wg(14)=0.09384; wg(15)=0.09563; wg(16)=0.09654; + wg(17)=0.09654; wg(18)=0.09563; wg(19)=0.09384; wg(20)=0.09117; wg(21)=0.08765; wg(22)=0.08331; wg(23)=0.07819; wg(24)=0.07234; + wg(25)=0.06582; wg(26)=0.05868; wg(27)=0.05099; wg(28)=0.04283; wg(29)=0.03427; wg(30)=0.02539; wg(31)=0.01627; wg(32)=0.00701; + + // evaluation points for l1 based on initial size class + for (int h=1;h<=nsex;h++) + for(int i=1; i<=32; i++) + for(int j=1; j<=nclass; j++) + l1_vec(h,j,i) = ((xg(i) + 1)/2)*(size_breaks(j+1)-size_breaks(j)) + size_breaks(j); + + if ( simflag ) + { + if ( !global_parfile ) + { + cerr << "Must have a gmacs.pin file to use the -sim command line option" << endl; + ad_exit(1); + } + cout << "|———————————————————————————————————————————|" << endl; + cout << "|*** RUNNING SIMULATION WITH RSEED = " << rseed << " ***|" << endl; + cout << "|———————————————————————————————————————————|" << endl; + simulation_model(); + //exit(1); + } + + cout << "+------------------------------+" << endl; + cout << "| Initial Procedure Section |" << endl; + cout << "+------------------------------+" << endl; + + initialize_model_parameters(); if ( verbose >= 3 ) cout << "Ok after initialize_model_parameters in Prelim ..." << endl; + calc_growth_increments(); if ( verbose >= 3 ) cout << "Ok after calc_growth_increments in Prelim ..." << endl; + calc_molting_probability(); if ( verbose >= 3 ) cout << "Ok after calc_molting_probability in Prelim ..." << endl; + calc_growth_transition(); if ( verbose >= 3 ) cout << "Ok after calc_growth_transition in Prelim ..." << endl; + calc_selectivities(); if ( verbose >= 3 ) cout << "Ok after calc_selectivities in Prelim ..." << endl; + +// ================================================================================================ + +PROCEDURE_SECTION + int Ipnt,ii,jj; + + if ( verbose >= 3 ) cout << "Ok after start of function ..." << endl; + + // Update function calls + NfunCall += 1; + + // Initialize model parameters + initialize_model_parameters(); if ( verbose >= 3 ) cout << "Ok after initialize_model_parameters ..." << endl; + + // Fishing fleet dynamics ... + if (current_phase() >= PhaseSelexPar) + calc_selectivities(); if ( verbose >= 3 ) cout << "Ok after calc_selectivities ..." << endl; + else + if ( verbose >= 3 ) cout << "Ok after ignoring selex calculation..." << endl; + + calc_fishing_mortality(); if ( verbose >= 3 ) cout << "Ok after calc_fishing_mortality ..." << endl; + calc_natural_mortality(); if ( verbose >= 3 ) cout << "Ok after calc_natural_mortality ..." << endl; + calc_total_mortality(); if ( verbose >= 3 ) cout << "Ok after calc_total_mortality ..." << endl; + + // growth ... + if (current_phase() >= PhaseGrowthPar) + { + calc_growth_increments(); if ( verbose >= 3 ) cout << "Ok after calc_growth_increments ..." << endl; + calc_molting_probability(); if ( verbose >= 3 ) cout << "Ok after calc_molting_probability ..." << endl; + calc_growth_transition(); if ( verbose >= 3 ) cout << "Ok after calc_growth_transition ..." << endl; + } + else + if ( verbose >= 3 ) cout << "Ok after ignoring growth calculation..." << endl; + + calc_recruitment_size_distribution(); if ( verbose >= 3 ) cout << "Ok after calc_recruitment_size_distribution ..." << endl; + calc_initial_numbers_at_length(); if ( verbose >= 3 ) cout << "Ok after calc_initial_numbers_at_length ..." << endl; + update_population_numbers_at_length(); if ( verbose >= 3 ) cout << "Ok after update_population_numbers_at_length ..." << endl; + calc_stock_recruitment_relationship(); if ( verbose >= 3 ) cout << "Ok after calc_stock_recruitment_relationship ..." << endl; + + // observation models ... + calc_predicted_catch(); if ( verbose >= 3 ) cout << "Ok after calc_predicted_catch ..." << endl; + calc_relative_abundance(); if ( verbose >= 3 ) cout << "Ok after calc_relative_abundance ..." << endl; + calc_predicted_composition(); if ( verbose >= 3 ) cout << "Ok after calc_predicted_composition ..." << endl; + if ( verbose >= 3 ) cout << "Ok after observation models ..." << endl; + + // objective function ... + calc_prior_densities(); if ( verbose >= 3 ) cout << "Ok after calc_prior_densities ..." << endl; + calc_objective_function(); if ( verbose >= 3 ) cout << "Ok after calc_objective_function ..." << endl; + + // sd_report variables + if ( sd_phase() ) + { + // Save the estimates parameters to ParsOut (used for variance estimation) + Ipnt = 0; + for (ii=1;ii<=ntheta;ii++) if (theta_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = theta(ii); } + for (ii=1;ii<=nGrwth; ii++) if (Grwth_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = Grwth(ii); } + for (ii=1;ii<=nslx_pars; ii++) if (slx_phzm(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_slx_pars(ii); } + for (ii=1;ii<=NumAsympRet; ii++) if (AsympSel_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = Asymret(ii); } + for (ii=1;ii<=nfleet; ii++) if (f_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_fbar(ii); } + for (ii=1;ii<=nfleet; ii++) + for (jj=1;jj<=nFparams(ii);jj++) if (f_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_fdev(ii,jj); } + for (ii=1;ii<=nfleet; ii++) if (foff_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_foff(ii); } + for (ii=1;ii<=nfleet; ii++) + if (nYparams(ii) > 0 & foff_phz(ii) > 0) + { + for (jj=1;jj<=nYparams(ii);jj++) {Ipnt +=1; ParsOut(Ipnt) = log_fdov(ii,jj); } + } + for (ii=1;ii<=nclass; ii++) if (rec_ini_phz > 0) {Ipnt +=1; ParsOut(Ipnt) = rec_ini(ii); } + for (ii=rdv_syr;ii<=rdv_eyr; ii++) if (rdv_phz > 0) {Ipnt +=1; ParsOut(Ipnt) = rec_dev_est(ii); } + for (ii=rdv_syr;ii<=rdv_eyr; ii++) if (rec_prop_phz > 0) {Ipnt +=1; ParsOut(Ipnt) = logit_rec_prop_est(ii); } + for (ii=1;ii<=nMdev; ii++) if (Mdev_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = m_dev_est(ii); } + for (ii=1;ii<=nSizeComps; ii++) if (nvn_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_vn(ii); } + for (ii=1;ii<=nSurveys; ii++) if (q_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = survey_q(ii); } + for (ii=1;ii<=nSurveys; ii++) if (cv_phz(ii) > 0) {Ipnt +=1; ParsOut(Ipnt) = log_add_cv(ii); } + + calc_spr_reference_points2(0); + if ( verbose >= 3 ) cout << "Ok after calc_spr_reference_points ..." << endl; + calc_sdreport(); + if ( verbose >= 3 ) cout << "Ok after calc_sdreport ..." << endl; + } + + // General outputs + if ( mceval_phase() ) write_eval(); + if ( NfunCall == StopAfterFnCall ) { CreateOutput(); exit(1); } + + +// ======================================================================================================================================= + + /** + * @brief Initialize model parameters + * @details Set global variable equal to the estimated parameter vectors. + * + **/ + +FUNCTION initialize_model_parameters + int Ipnt, Jpnt, Ihmo; + + // Get parameters from theta control matrix: + M0(1) = theta(1); + Jpnt = 2; + if (nsex>1) + { + if (MrelFem==YES) M0(2) = M0(1)*exp(theta(2)); + if (MrelFem==NO) M0(2) = theta(2); + Jpnt = 3; + } + logR0 = theta(Jpnt); + logRini = theta(Jpnt+1); + logRbar = theta(Jpnt+2); + ra(1) = theta(Jpnt+3); + rbeta(1) = theta(Jpnt+4); + Jpnt = Jpnt + 5; + if (nsex>1) + { + ra(2) = ra(1)*exp(theta(Jpnt)); + rbeta(2) = rbeta(1)*exp(theta(Jpnt+1)); + Jpnt += 2; + } + + logSigmaR = theta(Jpnt); + steepness = theta(Jpnt+1); + rho = theta(Jpnt+2); + Jpnt = Jpnt + 2; + + // Set rec_deviations + rec_dev.initialize(); + logit_rec_prop.initialize(); + for ( int i = rdv_syr; i <= rdv_eyr; i++) + { + rec_dev(i) = rec_dev_est(i); + logit_rec_prop(i) = logit_rec_prop_est(i); + } + + // Estimate initial numbers as absolute + if ( bInitializeUnfished == FREEPARS ) + { + Ipnt = 0; + for ( int h = 1; h <= nsex; h++ ) + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + Ihmo = pntr_hmo(h,m,o); + for ( int l = 1; l <= nclass; l++ ) + { + Ipnt += 1; + logN0(Ihmo,l) = theta(Jpnt+Ipnt); + } + } + } + + // Estimate initial numbers as logistic offsest + TempSS = 0; + if ( bInitializeUnfished == FREEPARSSCALED ) + { + Ipnt = 0; + for ( int h = 1; h <= nsex; h++ ) + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + Ihmo = pntr_hmo(h,m,o); + for ( int l = 1; l <= nclass; l++ ) + { + if (Ipnt==0) + logN0(Ihmo,1) = 0; + else + { logN0(Ihmo,l) = theta(Jpnt+Ipnt); + if (active(theta(Jpnt+Ipnt))) + TempSS += theta(Jpnt+Ipnt)*theta(Jpnt+Ipnt); } + Ipnt += 1; + } + } + } + + // Get Growth & Molting parameters + int icnt = 1; + for ( int h = 1; h <= nsex; h++ ) + for ( int igrow = 1; igrow<=nSizeIncVaries(h); igrow++) + { + if (bUseGrowthIncrementModel==LINEAR_GROWTHMODEL) + { + alpha(h) = Grwth(icnt); + beta(h) = Grwth(icnt+1); + gscale(h,igrow) = Grwth(icnt+2); + icnt += 3; + } + if (bUseGrowthIncrementModel==INDIVIDUAL_GROWTHMODEL1 | bUseGrowthIncrementModel==INDIVIDUAL_GROWTHMODEL2) + { + for (int l=1; l<=nclass;l++) molt_increment(h,igrow,l) = Grwth(icnt+l-1); + if (BetaParRelative==NO || igrow==1) + gscale(h,igrow) = Grwth(icnt+nclass); + else + if (BetaParRelative==YES) gscale(h,igrow) = exp(Grwth(icnt+nclass))*gscale(h,1); + icnt += (nclass+1); + } + + // Kappa varies + if (bUseGrowthIncrementModel==GROWTH_VARYK) + { + Linf(h,igrow) = Grwth(icnt); + Kappa(h,igrow) = Grwth(icnt+1); + SigmaKappa(h,igrow) = Grwth(icnt+2); + icnt += 3; + } + // Linf varies + if (bUseGrowthIncrementModel==GROWTH_VARYLINF) + { + Linf(h,igrow) = Grwth(icnt); + Kappa(h,igrow) = Grwth(icnt+1); + SigmaLinf(h,igrow) = Grwth(icnt+2); + icnt += 3; + } + // Linf and Kappa varies + if (bUseGrowthIncrementModel==GROWTH_VARYKLINF) + { + Linf(h,igrow) = Grwth(icnt); + Kappa(h,igrow) = Grwth(icnt+1); + SigmaLinf(h,igrow) = Grwth(icnt+2); + SigmaKappa(h,igrow) = Grwth(icnt+3); + icnt += 4; + } + } + for ( int h = 1; h <= nsex; h++ ) + for (int igrow=1;igrow<=nMoltVaries(h);igrow++) + if (bUseCustomMoltProbility == LOGISTIC_PROB_MOLT) + { + molt_mu(h,igrow) = Grwth(icnt); + molt_cv(h,igrow) = Grwth(icnt+1); + icnt = icnt + 2; + } + + // high grade factors + log_high_grade.initialize(); + int fleet; int sex; int year; + for (int i=1;i<=NumAsympRet;i++) + { + fleet = AsympSel_fleet(i); + sex = AsympSel_sex(i); + year = AsympSel_year(i); + log_high_grade(fleet,sex,year) = log(1.0-Asymret(i)); + } + + // m_dev parameters + for (int I = 1; I <=nMdev-nSizeDevs; I++) + { + if (Mdev_spec(I) >=0) + m_dev(I) = m_dev_est(I); + else + m_dev(I) = m_dev_est(-Mdev_spec(I)); + if (I <= nMdev_par_cnt(1)) + { m_dev_sex(1,I) = m_dev(I); } + else + { m_dev_sex(2,I-nMdev_par_cnt(1)) = m_dev(I); } + } + + // M multiplier + for (int l=1;l<=nclass;l++) Mmult(l) = 1; + if (nSizeDevs >= 1) + for (int ii=1;ii<=nSizeDevs;ii++) ///> Loop over changes in size + for (int l = m_size_nodeyear(ii);l <= nclass; l++) + Mmult(l) = m_dev_est(nMdev-nSizeDevs+ii); + +// ======================================================================================================================================= +// ======================================================================================================================================= + + /** + * @brief Calculate selectivies for each gear. + * @author Steve Martell, D'Arcy N. Webber + * @details Three selectivities must be accounted for by each fleet. + * 1) capture probability, 2) retention probability, and 3) release/discard probability. + * Only the parameters for capture probability and retention probability are estimated. + * The discard probability is calculated from these two probabilities. + * + * Maintain the possibility of estimating selectivity independently for each sex; assumes there are data to estimate female selex. + * + * Psuedocode: + * -# Loop over each gear: + * -# Create a pointer array with length = number of blocks + * -# Based on slx_type, fill pointer with parameter estimates. + * -# Loop over years and block-in the log_selectivity at mid points. + * + * Need to deprecate the abstract class for selectivity, 7X slower. + **/ +FUNCTION calc_selectivities + int h,i,k, k2; + dvar_vector pv; + dvariable p1, p2, p3; + dvar_vector temp_slx(1,3); + log_slx_capture.initialize(); + log_slx_discard.initialize(); + log_slx_retaind.initialize(); + + int j = 1; + for ( int k = 1; k <= nslx; k++ ) + { + class gsm::Selex *pSLX; + switch ( slx_type(k) ) + { + case SELEX_PARAMETRIC: ///> parametric + for (i = 1; i <= 3; i++) + { temp_slx(i) = log_slx_pars(j); j++; } + pv = temp_slx; + pSLX = new class gsm::ParameterPerClass(pv); + break; + case SELEX_COEFFICIENTS: ///> coefficients + pv = log_slx_pars(j); + pSLX = new class gsm::SelectivityCoefficients(pv); + break; + case SELEX_STANLOGISTIC: ///> logistic + p1 = mfexp(log_slx_pars(j)); + j++; + p2 = mfexp(log_slx_pars(j)); + j++; + pSLX = new class gsm::LogisticCurve(p1,p2); + break; + case SELEX_5095LOGISTIC: ///> logistic95 + p1 = mfexp(log_slx_pars(j)); + j++; + p2 = mfexp(log_slx_pars(j)); + j++; + pSLX = new class gsm::LogisticCurve95(p1,p2); + break; + case SELEX_DOUBLENORM: ///> double normal + p1 = mfexp(log_slx_pars(j)); + j++; + p2 = mfexp(log_slx_pars(j)); + j++; + p3 = mfexp(log_slx_pars(j)); + j++; + // AEP Fix needed + //pSLX = new class gsm::DoubleNormal(p1,p2,p3); + break; + case SELEX_UNIFORM1: // uniform 1 + pSLX = new class gsm::UniformCurve; + break; + case SELEX_UNIFORM0: // uniform 0 + pSLX = new class gsm::Uniform0Curve; + break; + } // switch + + int h1 = 1; + int h2 = nsex; + if ( slx_isex(k) == MALESANDCOMBINED ) { h2 = MALESANDCOMBINED; } ///> males (or combined sex) only + if ( slx_isex(k) == FEMALES ) { h1 = FEMALES; } ///> females only + for ( h = h1; h <= h2; h++ ) + { + for ( i = slx_styr(k); i <= slx_edyr(k); i++ ) + { + int kk = abs(slx_gear(k)); ///> fleet index (negative for retention) + if ( slx_gear(k) > 0 ) ///> capture selectvity + { + log_slx_capture(kk,h,i) = pSLX->logSelectivity(mid_points); + log_slx_capture(kk,h,i) -= log_slx_capture(kk,h,i,nclass); + } + else ///> discard (because the gear is NEGATIVE) + { + log_slx_retaind(kk,h,i) = pSLX->logSelectivity(mid_points); + if (slx_type(k)==SELEX_STANLOGISTIC || slx_type(k)==SELEX_5095LOGISTIC) + log_slx_retaind(kk,h,i) -= log_slx_retaind(kk,h,i,nclass); + log_slx_retaind(kk,h,i) += log_high_grade(kk,h,i); + log_slx_discard(kk,h,i) = log(1.0 - exp(log_slx_retaind(kk,h,i)) + TINY); + //cout << kk << " " << h << " " << i << " " << slx_type(k) << " " << log_slx_retaind(kk,h,i) << endl; + } + } + } + delete pSLX; + } // k + + // Sometimes one selectivity pattern is embedded in another + for ( int k = 1; k <= nslx; k++ ) + if (slx_incl(k) > 0 & slx_gear(k) > 0) ///> only for capture selectivity + { + int h1 = 1; + int h2 = nsex; + if ( slx_isex(k) == MALESANDCOMBINED ) { h2 = MALESANDCOMBINED; } ///> males (and combined) only + if ( slx_isex(k) == FEMALES ) { h1 = FEMALES; } ///> females only + for ( h = h1; h <= h2; h++ ) + { + int kk = abs(slx_gear(k)); ///> pointer to the fleet + k2 = slx_incl(k); ///> pointer to the fleet within within which this fleet falls + for ( i = slx_styr(k); i <= slx_edyr(k); i++ ) + for (int j=1;j<=nclass;j++) log_slx_capture(kk,h,i,j) += log_slx_capture(k2,h,i,j); + } + } + +// -------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate fishing mortality rates for each fleet. + * @details For each fleet estimate scaler log_fbar and deviates (f_devs). This function calculates the fishing mortality rate including deaths due to discards. Where xi is the discard mortality rate. + * + * In the event that there is effort data and catch data, then it's possible to estimate a catchability coefficient and predict the catch for the period of missing catch/discard data. Best option for this would be to use F = q*E, where q = F/E. Then in the objective function, minimize the variance in the estimates of q, and use the mean q to predict catch. Or minimize the first difference and assume a random walk in q. + * + * @param log_fbar are the mean fishing mortality of males parameters with dimension (1,nfleet,f_phz) + * @param log_fdev are the male fdevs parameters with dimension (1,nfleet,1,nFparams,f_phz) + * @param log_foff are the offset to the male fishing mortality parameters with dimension (1,nfleet,foff_phz) + * @param log_fdov are the female fdev offset parameters with dimension (1,nfleet,1,nYparams,foff_phz) + * @param dmr is the discard mortality rate + * @param F is the fishing mortality with dimension (1,nsex,syr,nyr,1,nseason,1,nclass) + **/ + + +FUNCTION calc_fishing_mortality + int ik,yk; + double xi; // discard mortality rate + dvar_vector sel(1,nclass); + dvar_vector ret(1,nclass); + dvar_vector vul(1,nclass); + + // Initilaize F2 with 1.0e-10 + F.initialize(); + dvariable log_ftmp; + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + for ( int l = 1; l <= nclass; l++) + F2(h,i,j,l) = 1.0e-10; + + // fishing morrtality generally + ft.initialize(); fout.initialize(); + for ( int k = 1; k <= nfleet; k++ ) + for ( int h = 1; h <= nsex; h++ ) + { + ik = 1; yk = 1; + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + { + if ( fhit(i,j,k)>0 ) + { + log_ftmp = log_fbar(k) + log_fdev(k,ik++); ///> Male F is the reference plus the annual deviation + fout(k,i) = exp(log_ftmp); ///> Report of male F + if (h==2) log_ftmp += log_foff(k); ///> Female F is an offset from male F + if (h==2 & yhit(i,j,k)>0) log_ftmp += log_fdov(k,yk++); ///> annual F dev + ft(k,h,i,j) = mfexp(log_ftmp); + xi = dmr(i,k); ///> Discard mortality rate + sel = mfexp(log_slx_capture(k,h,i))+1.0e-10; ///> Capture selectivity + ret = mfexp(log_slx_retaind(k,h,i)) * slx_nret(h,k); ///> Retension + vul = elem_prod(sel, ret + (1.0 - ret) * xi); ///> Vulnerability + F(h,i,j) += ft(k,h,i,j) * vul; ///> Fishing mortality + F2(h,i,j) += ft(k,h,i,j) * sel; ///> Contact mortality + } + } // years and seasons + } // fleet and sex + +// -------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate natural mortality array + * @details Natural mortality (M) is a 3d array for sex, year and size. + * + * todo: Size-dependent mortality + **/ + +FUNCTION calc_natural_mortality + + // reset M by sex, year and time + M.initialize(); + + // Add random walk to natural mortality rate + dvar_vector delta(syr+1,nyr); + + // Sex + for (int h=1;h<=nsex;h++) + { + M(h) = M0(h); + delta.initialize(); + switch( m_type ) + { + // case 0 (M_CONSTANT) not here as this is not evaluated if m_dev is not active + case M_RANDOM: ///> random walk in natural mortality + for (int iy=syr+1;iy<=nyr;iy++) delta(iy) = m_dev_sex(h,iy)-m_dev_sex(h,iy-1); + break; + case M_CUBIC_SPLINE: ///> cubic splines + { + dvector iyr = (m_nodeyear_sex(h) - syr) / (nyr - syr); + dvector jyr(syr+1,nyr); + jyr.fill_seqadd(0, 1.0 / (nyr - syr - 1)); + vcubic_spline_function csf(iyr, m_dev_sex(1)); + delta = csf(jyr); + } + break; + case M_BLOCKED_CHANGES: ///> Specific break points + for ( int idev = 1; idev <= nMdev_par_cnt(h); idev++ ) + delta(m_nodeyear_sex(1,idev)) = m_dev_sex(h,idev); + break; + // Modifying by Jie Zheng for specific time blocks + case M_TIME_BLOCKS1: // time blocks + for ( int idev = 1; idev <= nMdev_par_cnt(h); idev++ ) + { + // Is this syntax for split sex? + for ( int i = m_nodeyear_sex(h,1+(idev-1)*2); i <= m_nodeyear_sex(h,2+(idev-1)*2); i++ ) + M(h)(i) = mfexp(m_dev_sex(h,idev)); + } + break; + // Case for specific years + case M_TIME_BLOCKS3: // time blocks + for ( int idev = 1; idev <= nMdev_par_cnt(h); idev++ ) delta(m_nodeyear_sex(h,idev)) = m_dev_sex(1,idev); + for ( int i = syr+1; i <= nyr; i++ ) + M(h)(h) = M(h)(syr) * mfexp(delta(i)); // Deltas are devs from base value (not a walk) + break; + case M_TIME_BLOCKS2: // time blocks + for ( int idev = 1; idev <= nMdev_par_cnt(h)-1; idev++ ) + { + for ( int i = m_nodeyear_sex(h,idev); i < m_nodeyear_sex(h,idev+1); i++ ) + M(h)(i) = M(h)(i)*mfexp(m_dev_sex(h,idev)); + } + break; + } + // Update M by year. + if ( m_type < 4 ) + for ( int i = syr+1; i <= nyr; i++ ) M(h)(i) = M(h)(i-1) * mfexp(delta(i)); + + for (int i = syr; i <= nyr; i++) ///> Account for size-specific M + for (int l=1;l<=nclass;l++) M(h,i,l) *= Mmult(l); + } + + // Custom natural mortality input + if ( bUseCustomNaturalMortality == 1 ) + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr; i++ ) + M(h)(i) = CustomNaturalMortality(h)(i); + +// -------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate total instantaneous mortality rate and survival rate + * @details \f$ S = exp(-Z) \f$ + * + * ISSUE, for some reason the diagonal of S goes to NAN if linear growth model is used. Due to F. + * + * @param m_prop is a vector specifying the proportion of natural mortality (M) to be applied each season + * @return NULL + **/ + +FUNCTION calc_total_mortality + Z.initialize(); Z2.initialize();S.initialize(); + + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + { + Z(h,i,j) = m_prop(i,j) * M(h,i) + F(h,i,j); + Z2(h,i,j) = m_prop(i,j) * M(h,i) + F2(h,i,j); + if (season_type(j) == 0) for ( int l = 1; l <= nclass; l++ ) S(h,i,j)(l,l) = 1.0-Z(h,i,j,l)/Z2(h,i,j,l)*(1.0-mfexp(-Z2(h,i,j,l))); + if (season_type(j) == 1) for ( int l = 1; l <= nclass; l++ ) S(h,i,j)(l,l) = mfexp(-Z(h,i,j,l)); + } + +// ======================================================================================================================================= +// ======================================================================================================================================= + + /** + * @brief Calculate the probability of moulting by carapace width. + * @details Note that the parameters molt_mu and molt_cv can only be estimated in cases where there is new shell and old shell data. Note that the diagonal of the P matrix != 0, otherwise the matrix is singular in inv(P). + * + * @param molt_mu is the mean of the distribution + * @param molt_cv scales the variance of the distribution + **/ + +FUNCTION calc_molting_probability + double tiny = 0.000; + + molt_probability.initialize(); + + for ( int h = 1; h <= nsex; h++ ) + for ( int igrow=1;igrow<=nMoltVaries(h);igrow++) + { + // Pre-specified molt probability + if (bUseCustomMoltProbility==FIXED_PROB_MOLT) + { + for ( int i = syr; i <= nyr; i++ ) + { + if (igrow==1) + molt_probability(h)(i) = CustomMoltProbabilityMatrix(h,1); + else + { + if ( igrow > 1 && i >= iYrsMoltChanges(h,igrow-1)) + molt_probability(h)(i) = CustomMoltProbabilityMatrix(h,igrow); + } + } + } + // Uniform selectivity + if (bUseCustomMoltProbility == CONSTANT_PROB_MOLT) + { + for ( int i = syr; i <= nyr; i++ ) + for ( int l = 1; l <= nclass; l++ ) + molt_probability(h,i,l) = 1; + } + // Estimated logistic selectivity + if (bUseCustomMoltProbility == LOGISTIC_PROB_MOLT) + { + dvariable mu = molt_mu(h,1); + dvariable sd = mu * molt_cv(h,1); + for ( int i = syr; i <= nyr; i++ ) + { + if ( igrow > 1 && i >= iYrsMoltChanges(h,igrow-1) ) + { + mu = molt_mu(h,igrow); + sd = mu * molt_cv(h,igrow); + } + molt_probability(h)(i) = 1.0 - ((1.0 - 2.0 * tiny) * plogis(mid_points, mu, sd) + tiny); + } + } + } + +// ---------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Compute growth increments + * @details Presently based on liner form + * + * @param vSizes is a vector of doubles of size data from which to compute predicted values + * @param iSex is an integer vector indexing sex (1 = male, 2 = female) + * + * @return dvar_vector of predicted growth increments + **/ + +FUNCTION dvar_vector calc_growth_increments(const dvector vSizes, const ivector iSex) + { + if ( vSizes.indexmin() != iSex.indexmin() || vSizes.indexmax() != iSex.indexmax() ) + { cerr << "indices don't match..." << endl; ad_exit(1); } + RETURN_ARRAYS_INCREMENT(); + dvar_vector pMoltInc(1,vSizes.indexmax()); + pMoltInc.initialize(); + int h,i; + for ( i = 1; i <= nGrowthObs; i++ ) + { + h = iSex(i); + pMoltInc(i) = alpha(h) - beta(h) * vSizes(i); + } + RETURN_ARRAYS_DECREMENT(); + return pMoltInc; + } + +// ---------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Molt increment as a linear function of pre-molt size. + * + * TODO: Option for empirical molt increments. + * + * @param alpha + * @param beta + * @param mid_points + * + * @return molt_increment + **/ +FUNCTION calc_growth_increments + + if (bUseGrowthIncrementModel==LINEAR_GROWTHMODEL) + for ( int h = 1; h <= nsex; h++ ) + for ( int l = 1; l <= nclass; l++ ) + molt_increment(h,1,l) = alpha(h) - beta(h) * mid_points(l); + +// ---------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calclate the growth and size transtion matrix + * @details Calculates the size transition matrix for each sex based on growth increments, which is a linear function of the size interval, and the scale parameter for the gamma distribution. This function does the proper integration from the lower to upper size bin, where the mode of the growth increment is scaled by the scale parameter. + * + * This function loops over sex, then loops over the rows of the size transition matrix for each sex. The probability of transitioning from size l to size ll is based on the vector molt_increment and the scale parameter. In all there are three parameters that define the size transition matrix (alpha, beta, scale) for each sex. Issue 112 details some of evolution of code development here + * + * @param gscale + * @param P a 3D array of molting probabilities with dimension (1,nsex,1,nclass,1,nclass) + **/ +FUNCTION calc_growth_transition + int count68; + dvariable mean_size_after_molt; + dvariable Accum,CumInc,Upper_Inc; + dvar_vector psi(1,nclass+1); + dvar_vector sbi(1,nclass+1); + dvar_matrix gt(1,nclass,1,nclass); + dvariable Len1Low,Len1Hi,Len2Low,Len2Hi,total,step,prob_val,l1r; + dvariable mlk,sigmaK2,l1,rangL,rangU,Upp1,Kr,kval,temp,temp6,scale; + dvariable mll,sigmaL2,Linfval,Linfr,LinfU,LinfL,temp2,temp4,temp5; + dvariable templ11,templ12,temp68,tempr,nvar,tempL1,tempL2,tempk1,tempk2; + dvariable Temp,Val,Cum; + dvar_vector prob_val_vec(1,1024); + + // reset the growth transition matrix + growth_transition.initialize(); + + // loop over sex + for ( int h = 1; h <= nsex; h++ ) + { + // Set the growth-transition matrix (does not include molt_probability) + if ( bUseCustomGrowthMatrix == GROWTH_FIXEDGROWTHTRANS || bUseCustomGrowthMatrix == GROWTH_FIXEDSIZETRANS) + for (int j=1;j<=nSizeIncVaries(h);j++) growth_transition(h,j) = CustomGrowthMatrix(h,j); + + // Set the growth-transition matrix (size-increment is gamma) + if ( bUseCustomGrowthMatrix == GROWTH_INCGAMMA ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + gt.initialize(); + for ( int l = 1; l <= nSizeSex(h)-1; l++ ) + { + mean_size_after_molt = molt_increment(h,k,l) / gscale(h,k); + Accum = 0; + for ( int ll = l; ll <= nSizeSex(h)-1; ll++ ) + { + Upper_Inc = (size_breaks(ll+1) - mid_points(l))/gscale(h,k); + CumInc = cumd_gamma(Upper_Inc, mean_size_after_molt); + gt(l,ll) = CumInc - Accum; + Accum = CumInc; + } + gt(l,nSizeSex(h)) = 1.0 - Accum; + } + gt(nSizeSex(h),nSizeSex(h)) = 1.0; + growth_transition(h,k) = gt; + } + } + + // Set the growth-transition matrix (size after increment is gamma) + if ( bUseCustomGrowthMatrix == GROWTH_SIZEGAMMA ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + gt.initialize(); + sbi = size_breaks / gscale(h,k); + for ( int l = 1; l <= nSizeSex(h); l++ ) + { + mean_size_after_molt = (mid_points(l) + molt_increment(h,k,l)) / gscale(h,k); + psi.initialize(); + for (int ll = l; ll <= nclass+1; ll++ ) + psi(ll) = cumd_gamma(sbi(ll), mean_size_after_molt); + gt(l)(l,nSizeSex(h)) = first_difference(psi(l,nclass+1)); + gt(l)(l,nSizeSex(h)) = gt(l)(l,nSizeSex(h)) / sum(gt(l)); + } + growth_transition(h,k) = gt; + } + } + + // Set the growth-transition matrix (size after increment is normal) + if ( bUseCustomGrowthMatrix == GROWTH_NORMAL ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + gt.initialize(); + sbi = size_breaks / gscale(h,k); + for ( int l = 1; l <= nSizeSex(h)-1; l++ ) + { + mean_size_after_molt = mid_points(l) + molt_increment(h,k,l); + Temp = 0; + for ( int ll = l; ll <= nclass-1; ll++ ) + { + Val = (size_breaks(ll+1) - mean_size_after_molt)/gscale(h,k); + Cum = cumd_norm(Val); + gt(l)(ll) = Cum - Temp; + Temp = Cum; + } + gt(l,nclass) = 1.0-Temp; + } + gt(nclass,nclass) = 1.0; + growth_transition(h,k) = gt; + } + } + + // set the growth matrix based inidvidual variation in kappa + if ( bUseCustomGrowthMatrix == GROWTH_VARYK ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + mlk = log(Kappa(h,k)); + sigmaK2 = SigmaKappa(h,k)*SigmaKappa(h,k); + tempk2 = sqrt(2.0*M_PI*sigmaK2); + growth_transition(h,k,nSizeSex(h),nSizeSex(h)) = 1; // No growth from the last class + + // the initial size class + for (int l = 1; l <= nSizeSex(h)-1; l++ ) + { + Len1Low = size_breaks(l); Len1Hi = size_breaks(l+1); + scale = 1.0/(Len1Hi-Len1Low); + temp = Len1Low; total = 0; + if (Len1Low < Linf(h,k)) + { + for(int l2c=l;l2c<=nSizeSex(h)+20;l2c++) + { + if (l2c<=nSizeSex(h)) + step = size_breaks(l2c+1)-size_breaks(l2c); + else + step = size_breaks(nSizeSex(h)+1)-size_breaks(nSizeSex(h)); + l1r = step/2.0; + Len2Low = temp; Len2Hi = temp + step; temp = Len2Hi; + prob_val = 0; + for(int evl1=1;evl1<=32;evl1++) + { + l1 = ((xg(evl1) + 1.0)/2.0)*(Len1Hi-Len1Low) + Len1Low; + if (Linf(h,k) <= Len2Hi) Upp1 = Linf(h,k) - l1 - 0.001; else Upp1 = Len2Hi - l1; + rangU = -log(1 - Upp1/(Linf(h,k) - l1)); + if(Linf(h,k) > Len2Low) + { + rangL = -log(1 - (Len2Low - l1)/(Linf(h,k) - l1)); + Kr = (rangU-rangL)/2.0; + for( int evk=1; evk<=32;evk++) + { + kval = ((xg(evk) + 1.0)/2.0)*(rangU-rangL) + rangL; + if(kval > 0) + { + temp6 = mfexp(-((log(kval) - mlk)*(log(kval) - mlk))/(2.0*sigmaK2))/(kval*tempk2); + prob_val += Kr*wg(evk)*temp6*wg(evl1)*scale; + } + } // evk + } //if(Linf > Len2Low) + } // evl1 + prob_val *= l1r; + total += prob_val; + if(l2c < nSizeSex(h)) + growth_transition(h,k,l,l2c) = prob_val; + else + growth_transition(h,k,l,nSizeSex(h)) += prob_val; + } // l2c + for(int l2c=l;l2c<=nSizeSex(h);l2c++) growth_transition(h,k,l,l2c) /= total; + } // if (LenLow < Linf) + else + { + growth_transition(h,k,l,l) = 1; + total = 1; + } + } // l + } // k + } // if + + + // Linf varies among individuals + if ( bUseCustomGrowthMatrix == GROWTH_VARYLINF ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + mll = log(Linf(h,k)); + sigmaL2 = SigmaLinf(h,k)*SigmaLinf(h,k); + tempL1 = sqrt(2.0*M_PI*sigmaL2); + growth_transition(h,k,nSizeSex(h),nSizeSex(h)) = 1; // No growth from the last class + + // the initial size class + for ( int l = 1; l <= nSizeSex(h)-1; l++ ) + { + Len1Low = size_breaks(l); Len1Hi = size_breaks(l+1); + scale = 1.0/(Len1Hi-Len1Low); + temp = Len1Low; total = 0; + for(int l2c=l;l2c<=nSizeSex(h)+10;l2c++) + { + if (l2c<=nSizeSex(h)) + step = size_breaks(l2c+1)-size_breaks(l2c); + else + step = size_breaks(nSizeSex(h)+1)-size_breaks(nSizeSex(h)); + l1r = step/2.0; + Len2Low = temp; Len2Hi = temp + step; temp = Len2Hi; + prob_val = 0; + for(int evl1=1;evl1<=32;evl1++) + { + l1 = ((xg(evl1) + 1.0)/2.0)*(Len1Hi-Len1Low) + Len1Low; + LinfU = l1 + (Len2Hi - l1)/(1-mfexp(-Kappa(h,k))); + if(l2c == l) LinfL = l1; else LinfL = l1 + (Len2Low - l1)/(1-mfexp(-Kappa(h,k))); + temp2 = (log(l1) - mll)/SigmaLinf(h,k); + temp4 = 1.0 - cumd_norm(temp2); + Linfr = (LinfU - LinfL)/2.0; + for(int evL=1; evL<=32;evL++) + { + Linfval = ((xg(evL) + 1.0)/2.0)*(LinfU - LinfL) + LinfL; + temp5 = 1.0/(Linfval*tempL1)*mfexp(-((log(Linfval) - mll)*(log(Linfval) - mll))/(2*sigmaL2)); + prob_val += Linfr*wg(evL)*temp5*wg(evl1)*scale/temp4; + } // evl + } // evl1 + prob_val *= l1r; + total += prob_val; + if(l2c < nSizeSex(h)) + growth_transition(h,k,l,l2c) = prob_val; + else + growth_transition(h,k,l,nSizeSex(h)) += prob_val; + } // l2c + for(int l2c=l;l2c<=nSizeSex(h);l2c++) growth_transition(h,k,l,l2c) /= total; + + } // l + } // k + } // if + + // Linf and K vary among individuals + if ( bUseCustomGrowthMatrix == GROWTH_VARYKLINF ) + { + for (int k =1; k<=nSizeIncVaries(h);k++) + { + nvar = 15; + mll = log(Linf(h,k)); + mlk = log(Kappa(h,k)); + sigmaK2 = SigmaKappa(h,k)*SigmaKappa(h,k); + sigmaL2 = SigmaLinf(h,k)*SigmaLinf(h,k); + tempL1 = sqrt(2.0*M_PI*sigmaL2); + tempL2 = 2.0*sigmaL2; + tempk1 = sqrt(2.0*M_PI*sigmaK2); + tempk2 = 2.0*sigmaK2; + temp = sqrt(mfexp(2.0*mlk+sigmaK2)*(mfexp(sigmaK2)-1.0))*nvar; + rangU = Kappa(h,k) + temp; + rangL = Kappa(h,k) - temp; + if(rangL < 0) rangL = 0; + Kr = (rangU - rangL)/2.0; + growth_transition(h,k,nSizeSex(h),nSizeSex(h)) = 1; // No growth from the last class + + // the initial size class + for ( int l = 1; l <= nSizeSex(h)-1; l++ ) + { + Len1Low = size_breaks(l); Len1Hi = size_breaks(l+1); + temp = Len1Low; total = 0; + + scale = 1.0/(Len1Low-Len1Hi); + for(int l2c=l;l2c<=nSizeSex(h)+20;l2c++) + { + if (l2c<=nSizeSex(h)) + step = size_breaks(l2c+1)-size_breaks(l2c); + else + step = size_breaks(nSizeSex(h)+1)-size_breaks(nSizeSex(h)); + l1r = step/2.0; + templ11 = scale*Kr*l1r; + Len2Low = temp; Len2Hi = temp + step; temp = Len2Hi; prob_val = 0; + for(int evl1=1;evl1<=32;evl1++) + { + l1 = l1_vec(h,l,evl1); + temp2 = (log(l1) - mll)/SigmaLinf(h,k); + temp4 = 1.0 - cumd_norm(temp2); + templ12 = wg(evl1)*templ11/temp4; + count68 = 1; + for( int evk=1;evk<=32;evk++) + { + kval = ((xg(evk) + 1.0)/2.0)*(rangU-rangL) + rangL; + LinfU = l1 + (Len2Hi - l1)/(1.0-mfexp(-kval)); + if(l2c == l) LinfL = l1; else LinfL = l1 + (Len2Low - l1)/(1-mfexp(-kval)); + Linfr = (LinfU - LinfL)/2.0; + temp6 = mfexp(-((log(kval) - mlk)*(log(kval) - mlk))/tempk2)/(kval*tempk1); + temp68 = wg(evk)*temp6*templ12; + for(int evL=1;evL<=32;++evL) + { + Linfval = ((xg(evL) + 1.0)/2.0)*(LinfU - LinfL) + LinfL; + temp5 = mfexp(-((log(Linfval) - mll)*(log(Linfval) - mll))/tempL2)/(Linfval*tempL1); + prob_val_vec(count68) = Linfr*wg(evL)*temp5*temp68; + count68 += 1; + } + } + prob_val += sum(prob_val_vec); + } + + total += prob_val; + if(l2c < nSizeSex(h)) + growth_transition(h,k,l,l2c) = prob_val; + else + growth_transition(h,k,l,nSizeSex(h)) += prob_val; + } // l2c + for(int l2c=l;l2c<=nSizeSex(h);l2c++) growth_transition(h,k,l,l2c) /= total; + } // l + } // k + } // if + } // h + +// ============================================================================================================================================ + + /** + * @brief calculate size distribution for new recuits. + * @details Based on the gamma distribution, calculates the probability of a new recruit being in size-interval size. + * + * @param ra is the mean of the distribution + * @param rbeta scales the variance of the distribution + * @return rec_sdd the recruitment size distribution vector + **/ +FUNCTION calc_recruitment_size_distribution + dvariable ralpha; + dvar_vector x(1,nclass+1); + + rec_sdd.initialize(); + for ( int h=1; h <=nsex; h++) + { + ralpha = ra(h) / rbeta(h); + for ( int l = 1; l <= nclass+1; l++ ) x(l) = cumd_gamma(size_breaks(l) / rbeta(h), ralpha); + rec_sdd(h) = first_difference(x); + for (int l=nSizeClassRec(h)+1;l<=nclass;l++) rec_sdd(h,l) = 0; + rec_sdd(h) /= sum(rec_sdd(h)); // Standardize so each row sums to 1.0 + } + +// ============================================================================================================================================ + + /** + * @brief initialiaze populations numbers-at-length in syr + * @author Steve Martell + * @details This function initializes the populations numbers-at-length in the initial year of the model. + * + * Psuedocode: See note from Dave Fournier. + * + * For the initial numbers-at-length a vector of deviates is estimated, one for each size class, and have the option to initialize the model at unfished equilibrium, or some other steady state condition. + * + * Dec 11, 2014. Martell & Ianelli at snowgoose. We had a discussion regarding how to deal with the joint probability of molting and growing to a new size + * interval for a given length, and the probability of not molting. We settled on using the size-tranistion matrix to represent this joint probability, where the diagonal of the matrix to represent the probability of surviving and molting to a new size interval. The upper diagonal of the size-transition matrix represent the probability of growing to size interval j' given size interval j. + * + * Oldshell crabs are then the column vector of 1-molt_probabiltiy times the numbers-at-length, and the Newshell crabs is the column vector of molt_probability times the number-at-length. + * + * Jan 1, 2015. Changed how the equilibrium calculation is done. Use a numerical approach to solve the newshell oldshell initial abundance. + * + * Jan 3, 2015. Working with John Levitt on analytical solution instead of the numerical approach. Think we have a soln. + * + * Notation: + * n = vector of newshell crabs + * o = vector of oldshell crabs + * P = diagonal matrix of molting probabilities by size + * S = diagonal matrix of survival rates by size + * A = Size transition matrix. + * r = vector of new recruits (newshell) + * I = identity matrix. + * + * The following equations represent the dynamics of newshell and oldshell crabs. + * n = nSPA + oSPA + r (1) + * o = oS(I-P)A + nS(I-P)A (2) + * Objective is to solve the above equations for n and o repsectively. Starting with o: + * o = n(I-P)S[I-(I-P)S]^(-1) (3) + * next substitute (3) into (1) and solve for n + * n = nPSA + n(I-P)S[I-(I-P)S]^(-1)PSA + r + * let B = [I-(I-P)S]^(-1) + * n - nPSA - n(I-P)SBPSA = r + * n(I - PSA - (I-P)SBPSA) = r + * let C = (I - PSA - (I-P)SBPSA) + * then n = C^(-1) r (4) + * + * April 28, 2015. There is no case here for initializing the model at unfished equilibrium conditions. Need to fix this for SRA purposes. SJDM. + * + * @param bInitializeUnfished + * @param logR0 + * @param logRini + * @param rec_sdd is the vector of recruitment size proportions. It has dimension (1,nclass) + * @param rec_ini is the vector of initial size deviations. It has dimension (1,nclass) + * @param M is a 3D array of the natural mortality. It has dimension (1,nsex,syr,nyr,1,nclass) + * @param S is a 5D array of the survival rate (where S=exp(-Z)). It has dimension (1,nsex,syr,nyr,1,nseason,1,nclass,1,nclass) + * @param d4_N is the numbers in each group (sex/maturity/shell), year, season and length. It has dimension (1,n_grp,syr,nyr+1,1,nseason,1,nclass) + **/ +FUNCTION calc_initial_numbers_at_length + dvariable log_initial_recruits, scale; + + // Reset the probability of molting to first year value + ProbMolt.initialize(); + for (int h = 1; h <= nsex; h++ ) + for (int l=1;l<=nclass;l++) + ProbMolt(h,l,l) = molt_probability(h,syr,l); + + // Initial recruitment + switch( bInitializeUnfished ) + { + case UNFISHEDEQN: ///> Unfished conditions + log_initial_recruits = logR0; + break; + case FISHEDEQN: ///> Steady-state fished conditions + log_initial_recruits = logRini; + break; + case FREEPARS: //> Free parameters + log_initial_recruits = logN0(1,1); + break; + case FREEPARSSCALED: ///> Free parameters revised + log_initial_recruits = logRini; + break; + } + for ( int h = 1; h <= nsex; h++ ) + { recruits(h)(syr) = mfexp(log_initial_recruits); } + + // Analytical equilibrium soln + int ig; + d4_N.initialize(); + dmatrix Id = identity_matrix(1,nclass); + dvar_matrix x(1,n_grp,1,nclass); + dvar_vector y(1,nclass); + dvar_matrix A(1,nclass,1,nclass); + dvar_matrix _S(1,nclass,1,nclass); + _S.initialize(); + + Eqn_basis = CONSTANTREC; ///> Need to run brute force with constant recruitment + switch( bInitializeUnfished ) + { + case UNFISHEDEQN: ///> Unfished conditions + bSteadyState = UNFISHEDEQN; + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = -100; + x = calc_brute_equilibrium(syr,200); + for ( int ig = 1; ig <= n_grp; ig++ ) d4_N(ig)(syr)(1) = x(ig); + break; + case FISHEDEQN: ///> Steady-state fished conditions + bSteadyState = FISHEDEQN; + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = log(finit(k)+1.0e-10); + x = calc_brute_equilibrium(syr,200); + for ( int ig = 1; ig <= n_grp; ig++ ) d4_N(ig)(syr)(1) = x(ig); + break; + case FREEPARS: ///> Free parameters + // Single shell condition and sex + for ( int h = 1; h <= nsex; h++ ) + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + d4_N(ig)(syr)(1) = mfexp(logN0(ig)); + } + break; + case FREEPARSSCALED: ///> Free parameters (revised + scale = sum(exp(logN0)); + for ( int h = 1; h <= nsex; h++ ) + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + d4_N(ig)(syr)(1) = mfexp(log_initial_recruits+logN0(ig))/scale; + } + break; + } + +// -------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Update numbers-at-length + * @author Team + * @details Numbers at length are propagated each year for each sex based on the size transition matrix and a vector of size-specifc survival rates. The columns of the size-transition matrix are multiplied by the size-specific survival rate (a scalar). New recruits are added based on the estimated average recruitment and annual deviate, multiplied by a vector of size-proportions (rec_sdd). + * + * @param bInitializeUnfished + * @param logR0 + * @param logRbar + * @param d4_N is the numbers in each group (sex/maturity/shell), year, season and length. It has dimension (1,n_grp,syr,nyr+1,1,nseason,1,nclass) + * @param recruits is the vector of average recruits each year. It has dimension (syr,nyr) + * @param rec_dev is an init_bounded_dev_vector of recruitment deviation parameters. It has dimension (syr+1,nyr,-7.0,7.0,rdv_phz) + * @param rec_sdd is the vector of recruitment size proportions. It has dimension (1,nclass) + **/ +FUNCTION update_population_numbers_at_length + int h,i,ig,o,m,isizeTrans; + + dmatrix Id = identity_matrix(1,nclass); + dvar_vector rt(1,nclass); + dvar_vector x(1,nclass); + dvar_vector y(1,nclass); + dvar_vector z(1,nclass); + + // this is what should be used because recruitment is not always during the first season (i.e. during the initial conditions) + for ( i = syr; i <= nyr; i++ ) + for ( h = 1; h <= nsex; h++ ) + { + if ( bInitializeUnfished == UNFISHEDEQN ) + recruits(h,i) = mfexp(logR0)*float(nsex); + else + recruits(h,i) = mfexp(logRbar)*float(nsex); + // This splits recruitment out proportionately into males and females + if (h == MALESANDCOMBINED) recruits(h)(i) *= mfexp(rec_dev(i)) * 1.0 / (1.0 + mfexp(-logit_rec_prop(i))); + if (h == FEMALES) recruits(h)(i) *= mfexp(rec_dev(i)) * (1.0 - 1.0 / (1.0 + mfexp(-logit_rec_prop(i)))); + } + + for ( i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + { + for ( ig = 1; ig <= n_grp; ig++ ) + { + h = isex(ig); + isizeTrans = iYrIncChanges(h,i); + m = imature(ig); + o = ishell(ig); + + x = d4_N(ig)(i)(j); + // Mortality (natural and fishing) + x = x * S(h)(i)(j); + if ( nshell == 1 ) + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1.0 - molt_probability(h)(i)); // did not molt, become oldshell + x = elem_prod(x, molt_probability(h)(i)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + x = x + y; + } + // Recruitment + if (j == season_recruitment) x += recruits(h)(i) * rec_sdd(h); + if (j == nseason) d4_N(ig)(i+1)(1) = x; else d4_N(ig)(i)(j+1) = x; + } + else + { + if ( o == 1 ) // newshell + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1.0 - molt_probability(h)(i)); // did not molt, become oldshell + x = elem_prod(x, molt_probability(h)(i)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + } + // Recruitment + if (j == season_recruitment) x += recruits(h)(i) * rec_sdd(h); + if (j == nseason) d4_N(ig)(i+1)(1) = x; else d4_N(ig)(i)(j+1) = x; + } + if ( o == 2 ) // oldshell + { + // Molting and growth + z.initialize(); + if (j == season_growth) + { + z = elem_prod(x, molt_probability(h)(i)) * growth_transition(h,isizeTrans); // molted and grew, become newshell + x = elem_prod(x, 1 - molt_probability(h)(i)) + y; // did not molt, remain oldshell and add the newshell that become oldshell + } + if (j == nseason) + { d4_N(ig-1)(i+1)(1) += z; d4_N(ig)(i+1)(1) = x; } + else + { d4_N(ig-1)(i)(j+1) += z; d4_N(ig)(i)(j+1) = x; } + } + } + + if ( o == 1 && m == 2 ) // terminal molt to new shell. + { + } + if ( o == 2 && m == 2 ) // terminal molt newshell to oldshell. + { + } + } // ig + } // i and j + +// ================================================================================================================================================= + + /** + * @brief Calculate predicted catch observations + * @details The function uses the Baranov catch equation to predict the retained and discarded catch. + * + * Assumptions: + * 1) retained (landed catch) is assume to be newshell male only. + * 2) discards are all females (new and old) and male only crab. + * 3) Natural and fishing mortality occur simultaneously. + * 4) discard is the total number of crab caught and discarded. + **/ +FUNCTION calc_predicted_catch + int nhit; ///> number of values for computing q + double cobs, effort; ///> used when computing q + dvariable tmp_ft,totalnalobs,totalnalpre; ///> temp variables + dvar_vector sel(1,nclass); ///> capture selectivity + dvar_vector nal(1,nclass); ///> numbers or biomass at length. + dvar_vector tempZ1(1,nclass); ///> total mortality + + // First need to calculate a catchability (q) for each catch data frame if there is any catch and effort (must be both) + log_q_catch.initialize(); + for ( int kk = 1; kk <= nCatchDF; kk++ ) + { + nhit = 0; + for ( int jj = 1; jj <= nCatchRows(kk); jj++ ) + { + cobs = obs_catch(kk,jj); ///> catch data + effort = dCatchData(kk,jj,10); ///> Effort data + + if (cobs > 0.0 && effort > 0.0) ///> There are catch and effort data + { + int i = dCatchData(kk,jj,1); ///> year index + int j = dCatchData(kk,jj,2); ///> season index + int k = dCatchData(kk,jj,3); ///> fleet/gear index + int h = dCatchData(kk,jj,4); ///> sex index + int type = int(dCatchData(kk,jj,7)); ///> Type of catch (total= 0, retained = 1, discard = 2) + int unit = int(dCatchData(kk,jj,8)); ///> Units of catch equation (1 = biomass, 2 = numbers) + + if ( h != BOTHSEX ) ///> sex specific + { + log_q_catch(kk) += log(ft(k,h,i,j) / effort); + nhit += 1; + } + else // sexes combinded + { + totalnalobs = 0; totalnalpre = 0; + for ( h = 1; h <= nsex; h++ ) + { + sel = log_slx_capture(k,h,i); ///> Capture seletiity + switch( type ) + { + case RETAINED: // retained catch + sel = mfexp(sel + log_slx_retaind(k,h,i)); + break; + case DISCARDED: // discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k,h,i))); + break; + case TOTALCATCH: // total catch + sel = mfexp(sel); + break; + } + nal.initialize(); ///> Computer numbers + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + tmp_ft = ft(k,h,i,j); + + if (season_type(j)==INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j); + totalnalobs += nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + totalnalpre += nal * elem_div(elem_prod(effort * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + } + log_q_catch(kk) += log(totalnalobs / totalnalpre); + nhit += 1; + } // h + } // cobs + } // jj + if ( nhit > 0 ) log_q_catch(kk) /= nhit; + } + + // Now make predictions + res_catch.initialize(); + pre_catch.initialize(); + obs_catch_effort.initialize(); + for ( int kk = 1; kk <= nCatchDF; kk++ ) + { + for ( int jj = 1; jj <= nCatchRows(kk); jj++ ) + { + int i = dCatchData(kk,jj,1); ///> year index + int j = dCatchData(kk,jj,2); ///> season index + int k = dCatchData(kk,jj,3); ///> fleet/gear index + int h = dCatchData(kk,jj,4); ///> sex index + int type = int(dCatchData(kk,jj,7)); ///> Type of catch (total= 0, retained = 1, discard = 2) + int unit = int(dCatchData(kk,jj,8)); ///> Units of catch equation (1 = biomass, 2 = numbers) + effort = dCatchData(kk,jj,10); ///> Effort data + cobs = obs_catch(kk,jj); ///> catch data + + if ( h!=BOTHSEX ) // sex specific + { + sel = log_slx_capture(k,h,i); ///> Capture selectivity + //ret = log_slx_retaind(k,h,i); ///> Retention probability + //dis = log_slx_discard(k,h,i); ///> Discard fraction + switch ( type ) + { + case RETAINED: ///> retained catch + sel = mfexp(sel + log_slx_retaind(k,h,i)); + break; + case DISCARDED: ///> discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k,h,i))); + break; + case TOTALCATCH: ///> total catch + sel = mfexp(sel); + break; + } + // sum of nals + nal.initialize(); ///> Computer numbers + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + if (season_type(j)==INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j); + + // predict catch + tmp_ft = ft(k,h,i,j); /// > Extract F + pre_catch(kk,jj) += nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + if (cobs == 0 & effort > 0.0) + obs_catch_effort(kk,jj) += nal * elem_div(elem_prod(mfexp(log_q_catch(kk)) * effort * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + } + else // sexes combined + { + for ( h = 1; h <= nsex; h++ ) + { + sel = log_slx_capture(k)(h)(i); + switch( type ) + { + case RETAINED: // retained catch + sel = mfexp(sel + log_slx_retaind(k,h,i)); + break; + case DISCARDED: // discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k,h,i))); + break; + case TOTALCATCH: // total catch + sel = mfexp(sel); + break; + } + // sum of nals + nal.initialize(); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + + tmp_ft = ft(k,h,i,j); /// > Extract F + if (season_type(j)==INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j); + pre_catch(kk,jj) += nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + if (cobs == 0 & effort > 0.0) + obs_catch_effort(kk,jj) += nal * elem_div(elem_prod(mfexp(log_q_catch(kk)) * effort * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + } + } // sex-specific + + // Catch residuals + // In first case (obs_catch > 0) then if there is only catch data, calculate the residual as per usual; if there is catch and effort data, then still use observed catch to calculate the residual, despite the predicted catch being calculated differently. + // In second case, when effort > 0 then the residual is the pred catch using Fs - pred catch using q + if ( obs_catch(kk,jj) > 0.0 ) + { + res_catch(kk,jj) = log(obs_catch(kk,jj)) - log(pre_catch(kk,jj)); + } + else + if (effort > 0.0) + { + res_catch(kk,jj) = log(obs_catch_effort(kk,jj)) - log(pre_catch(kk,jj)); + } + } // lines of catch + } // data block + +// ---------------------------------------------------------------------------------------------- + + /** + * @brief Calculate predicted catch for a combination of years + * @author Andre Punt + * @details The function uses the Baranov catch equation to predict the retained, discarded, or total catch + * year i; season j; sex h; gear k; type (1=retained;2=discards;3=total); unit (1=mass;2=numbers) + **/ +FUNCTION dvariable calc_predicted_catch_det(const int i, const int j, const int h, const int k, const int type, const int unit) + { + dvariable tmp_ft,out; + dvar_vector sel(1,nclass); ///> capture selectivity + dvar_vector nal(1,nclass); ///> numbers or biomass at length. + dvar_vector tempZ1(1,nclass); ///> total mortality + + nal.initialize(); + sel = log_slx_capture(k,h,i); + switch( type ) + { + case RETAINED: // retained catch + sel = mfexp(sel + log_slx_retaind(k,h,i)); + break; + case DISCARDED: // discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k,h,i))); + break; + case TOTALCATCH: // total catch + sel = mfexp(sel); + break; + } + + nal.initialize(); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + + tmp_ft = ft(k,h,i,j); /// > Extract F + if (season_type(j) == INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j); + out = nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + return(out); + } + +// ---------------------------------------------------------------------------------------------- + + /** + * @brief Calculate predicted catch for all years (not just data years) + * @author D'Arcy N. Webber + * @details The function uses the Baranov catch equation to predict the retained and discarded catch for all model years (not just those years for which we have observations). This is used for plotting purposes only. + **/ +FUNCTION calc_predicted_catch_out + dvariable tmp_ft,out2; + dvar_vector sel(1,nclass); ///> capture selectivity + dvar_vector nal(1,nclass); ///> numbers or biomass at length. + dvar_vector tempZ1(1,nclass); ///> total mortality + + pre_catch_out.initialize(); + for ( int i = syr; i <= nyr; i++ ) + { + for ( int kk = 1; kk <= nCatchDF; kk++ ) + { + int j = dCatchData_out(kk,i,2); ///> season index + int k = dCatchData_out(kk,i,3); ///> fleet/gear index + int h = dCatchData_out(kk,i,4); ///> sex index + int type = int(dCatchData_out(kk,i,7)); ///> Type of catch (total= 0, retained = 1, discard = 2) + int unit = int(dCatchData_out(kk,i,8)); ///> Units of catch equation (1 = biomass, 2 = numbers) + + if ( h != BOTHSEX ) ///> sex specific + { + sel = log_slx_capture(k,h,i); + switch ( type ) + { + case RETAINED: // retained catch + sel = mfexp(sel + log_slx_retaind(k,h,i)); + break; + case DISCARDED: // discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k,h,i))); + break; + case TOTALCATCH: // total catch + sel = mfexp(sel); + break; + } + + nal.initialize(); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + + tmp_ft = ft(k,h,i,j); + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + if (season_type(j)==INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j)+1.0e-10; + pre_catch_out(kk,i) += nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + } + else + { + // sexes combined + out2 = 0; + for ( int h = 1; h <= nsex; h++ ) + { + sel = log_slx_capture(k)(h)(i); + switch( type ) + { + case RETAINED: // retained catch + sel = mfexp(sel + log_slx_retaind(k)(h)(i)); + break; + case DISCARDED: // discarded catch + sel = elem_prod(mfexp(sel), 1.0 - mfexp(log_slx_retaind(k)(h)(i))); + break; + case TOTALCATCH: // total catch + sel = mfexp(sel); + break; + } + + nal.initialize(); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_N(ig,i,j); } + nal = (unit == 1) ? elem_prod(nal, mean_wt(h,i)) : nal; + + tmp_ft = ft(k,h,i,j); + if (season_type(j)==INSTANT_F) tempZ1 = Z2(h,i,j); else tempZ1 = Z(h,i,j); + pre_catch_out(kk,i) += nal * elem_div(elem_prod(tmp_ft * sel, 1.0 - mfexp(-tempZ1)), tempZ1); + //out2 += calc_predicted_catch_det(i, j, h, k, type, unit); + } // h + } // sex + } // kk + } // i + + res_catch_out.initialize(); + for ( int kk = 1; kk <= nCatchDF; kk++ ) + for ( int i = syr; i <= nyr; i++ ) + if ( obs_catch_out(kk,i) > 0.0 && pre_catch_out(kk,i) > 0.0 ) + res_catch_out(kk,i) = log(obs_catch_out(kk,i)) - log(pre_catch_out(kk,i)); ///> Catch residuals + +// ================================================================================================================================================= + + /** + * @brief Calculate predicted relative abundance and residuals + * @author Steve Martell, D'Arcy Webber + * + * @details This function uses the conditional mle for q to scale the population to the relative abundance index. Assumed errors in relative abundance are lognormal. Currently assumes that the CPUE index is made up of both retained and discarded crabs. + * + * Question regarding use of shell condition in the relative abundance index. Currenlty there is no shell condition information in the CPUE data, should there be? Similarly, there is no mature immature information, should there be? + **/ +FUNCTION calc_relative_abundance + int unit; + dvar_vector sel(1,nclass); ///> capture selectivity + dvar_vector nal(1,nclass); ///> numbers or biomass at length. + + for ( int k = 1; k <= nSurveys; k++ ) + { + dvar_vector V(1,nSurveyRows(k)); + V.initialize(); + for ( int jj = 1; jj <= nSurveyRows(k); jj++ ) + { + nal.initialize(); + int i = dSurveyData(k,jj,1); ///> year index + int j = dSurveyData(k,jj,2); ///> season index + int g = dSurveyData(k,jj,3); ///> fleet/gear index + int h = dSurveyData(k,jj,4); ///> sex index + int unit = dSurveyData(k,jj,7); ///> units 1 = biomass 2 = numbers + + if ( h != BOTHSEX ) ///> single-sex index + { + sel = mfexp(log_slx_capture(g)(h)(i)); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + nal += ( unit == 1 ) ? elem_prod(d4_N(ig,i,j), mean_wt(h,i)) : d4_N(ig,i,j); + } + V(jj) = nal * sel; + } + else + { + for (int h = 1; h <= nsex; h++ ) ///> Combined sex index + { + sel = mfexp(log_slx_capture(g)(h)(i)); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + nal += ( unit == 1 ) ? elem_prod(d4_N(ig,i,j), mean_wt(h,i)) : d4_N(ig,i,j); + } + V(jj) += nal * sel; + } + } + } // nSurveyRows(k) + + // Do we need an analystical Q + switch ( q_anal(k) ) + { + case 0: ///> q as a parameter + pre_cpue(k) = survey_q(k) * V; + res_cpue(k) = log(obs_cpue(k)) - log(pre_cpue(k)); + break; + case 1: ///> analytic q + dvar_vector zt = log(obs_cpue(k)) - log(V); + dvariable zbar = mean(zt); + res_cpue(k) = zt - zbar; + survey_q(k) = mfexp(zbar); + pre_cpue(k) = survey_q(k) * V; + break; + } + } + +// ================================================================================================================================================= + + /** + * @brief Calculate predicted size composition data. + * + * @details Predicted size composition data are given in proportions. + * Size composition strata: + * - sex (0 = both sexes, 1 = male, 2 = female) + * - type (0 = all catch, 1 = retained, 2 = discard) + * - shell condition (0 = all, 1 = new shell, 2 = oldshell) + * - mature or immature (0 = both, 1 = immature, 2 = mature) + * + * Jan 5, 2015. + * Size compostion data can come in a number of forms. + * Given sex, maturity and 3 shell conditions, there are 12 possible + * combinations for adding up the numbers at length (nal). + * + * Shell + * Sex Maturity condition Description + * _____________________________________________________________ + * Male 0 1 immature, new shell + * ! Male 0 2 immature, old shell + * ! Male 0 0 immature, new & old shell 1 Male, immature, new shell + * Male 1 1 mature, new shell + * Male 1 2 mature, old shell + * Male 1 0 mature, new & old shell + * Female 0 1 immature, new shell + * !Female 0 2 immature, old shell + * !Female 0 0 immature, new & old shell + * Female 1 1 mature, new shell + * Female 1 2 mature, old shell + * Female 1 0 mature, new & old shell + * _____________________________________________________________ + * + * Call function to get the appropriate numbers-at-length. + * + * TODO: + * [ ] Add maturity component for data sets with mature old and mature new. + **/ +FUNCTION calc_predicted_composition + dvar_vector dNtmp(1,nclass); ///> temporary Ns + dvar_vector nal(1,nclass); ///> numbers or biomass at length. + + d3_pre_size_comps_in.initialize(); + d3_pre_size_comps.initialize(); + for ( int ii = 1; ii <= nSizeComps_in; ii++ ) + { + int nbins = nSizeCompCols_in(ii); + for ( int jj = 1; jj <= nSizeCompRows_in(ii); jj++ ) + { + dNtmp.initialize(); + int i = d3_SizeComps_in(ii)(jj,-7); ///> year + int j = d3_SizeComps_in(ii)(jj,-6); ///> seas + int k = d3_SizeComps_in(ii)(jj,-5); ///> gear (a.k.a. fleet) + int h = d3_SizeComps_in(ii)(jj,-4); ///> sex + int type = d3_SizeComps_in(ii)(jj,-3); ///> retained or discard + int shell = d3_SizeComps_in(ii)(jj,-2); ///> shell condition + int bmature = d3_SizeComps_in(ii)(jj,-1); ///> boolean for maturity + + if ( h != BOTHSEX ) ///> sex specific + { + dvar_vector sel = mfexp(log_slx_capture(k,h,i)); + dvar_vector ret = mfexp(log_slx_retaind(k,h,i)); + dvar_vector dis = mfexp(log_slx_discard(k,h,i)); + + nal.initialize(); ///> Numbers by sex + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + if ( shell == 0 ) nal += d4_N(ig,i,j); + if ( shell == o ) nal += d4_N(ig,i,j); + } + + switch ( type ) + { + case RETAINED: ///> retained + dNtmp += elem_prod(nal, elem_prod(sel, ret)); + break; + case DISCARDED: ///> discarded + dNtmp += elem_prod(nal, elem_prod(sel, dis)); + break; + case TOTALCATCH: ///> both retained and discarded + dNtmp += elem_prod(nal, sel); + break; + } + } + else + { // sexes combined in the observations + for ( int h = 1; h <= nsex; h++ ) + { + dvar_vector sel = mfexp(log_slx_capture(k,h,i)); + dvar_vector ret = mfexp(log_slx_retaind(k,h,i)); + dvar_vector dis = mfexp(log_slx_discard(k,h,i)); + + nal.initialize(); ///> Numbers by sex + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { + int ig = pntr_hmo(h,m,o); + if ( shell == 0 ) nal += d4_N(ig,i,j); + if ( shell == o ) nal += d4_N(ig,i,j); + } + + switch ( type ) + { + case RETAINED: ///> retained + dNtmp += elem_prod(nal, elem_prod(sel, ret)); + break; + case DISCARDED: ///> discarded + dNtmp += elem_prod(nal, elem_prod(sel, dis)); + break; + case TOTALCATCH: ///> both retained and discarded + dNtmp += elem_prod(nal, sel); + break; + } + } + } + d3_pre_size_comps_in(ii)(jj) = dNtmp(1,nbins); + d3_pre_size_comps_in(ii)(jj)(nbins) = sum(dNtmp(nbins,nclass)); + } + } + + // This aggregates the size composition data by appending size compositions horizontally + int oldk = 9999; int j; int i; + for ( int kk = 1; kk <= nSizeComps_in; kk++ ) + { + int k = iCompAggregator(kk); + if ( oldk != k ) j = 0; + oldk = k; + for ( int jj = 1; jj <= nSizeCompCols_in(kk); jj++ ) + { + j += 1; + for ( int ii = 1; ii <= nSizeCompRows_in(kk); ii++ ) + { + i = ii; + d3_pre_size_comps(k,i,j) = d3_pre_size_comps_in(kk,ii,jj); + } + } + } + + // This normalizes all observations by row + for ( int k = 1; k <= nSizeComps; k++ ) + for ( int i = 1; i <= nSizeCompRows(k); i++ ) + d3_pre_size_comps(k,i) /= sum(d3_pre_size_comps(k,i)); + +// ================================================================================================================================================= + + /** + * @brief Calculate stock recruitment relationship. + * @details Assuming a Beverton-Holt relationship between the mature biomass (user defined) and the annual recruits. Note that we derive so and bb in R = so MB / (1 + bb * Mb) from Ro and steepness (leading parameters defined in theta). + * + * NOTES: if nSRR_flag == 1 then use a Beverton-Holt model to compute the recruitment deviations for minimization. + **/ +FUNCTION calc_stock_recruitment_relationship + dvariable so, bb; + dvariable ro = mfexp(logR0); + dvariable phiB; + double lam; + dvariable reck = 4.*steepness/(1.-steepness); + dvar_matrix _A(1,nclass,1,nclass); + dvar_matrix _S(1,nclass,1,nclass); + + // Reset the probability of molting to first year value + ProbMolt.initialize(); + for (int h = 1; h <= nsex; h++ ) + for (int l=1;l<=nclass;l++) + ProbMolt(h,l,l) = molt_probability(h,syr,l); + + // get unfished mature male biomass per recruit. + phiB = 0.0; + _A.initialize(); + _S.initialize(); + for( int h = 1; h <= nsex; h++ ) + { + for ( int l = 1; l <= nclass; ++l ) _S(l,l) = mfexp(-M(h)(syr)(l)); + _A = growth_transition(h,1); + dvar_vector x(1,nclass); + dvar_vector y(1,nclass); + + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + + // Single shell condition + if ( nshell == 1 && nmature == 1 ) + { + calc_equilibrium(x,_A,_S,rec_sdd(h)); + phiB += lam * x * elem_prod(mean_wt(h)(syr), maturity(h)); + } + // Continuous molt (newshell/oldshell) + if ( nshell == 2 && nmature == 1 ) + { + calc_equilibrium(x,y,_A,_S,ProbMolt(h),rec_sdd(h)); + phiB += lam * x * elem_prod(mean_wt(h)(syr), maturity(h)) + lam * y * elem_prod(mean_wt(h)(syr), maturity(h)); + } + // Insert terminal molt case here + + } + dvariable bo = ro * phiB; + + so = reck * ro / bo; + bb = (reck - 1.0) / bo; + + dvar_vector ssb = calc_ssb().shift(syr+1); + dvar_vector rhat = elem_div(so * ssb , 1.0 + bb* ssb); + + // residuals + int byr = syr + 1; + res_recruit.initialize(); + dvariable sigR = mfexp(logSigmaR); + dvariable sig2R = 0.5 * sigR * sigR; + + switch ( nSRR_flag ) + { + case 0: // NO SRR + res_recruit(byr,nyr) = log(recruits(1)(byr,nyr)) - (1.0-rho) * logRbar - rho * log(++recruits(1)(byr-1,nyr-1)) + sig2R; + break; + case 1: // SRR model + res_recruit(byr,nyr) = log(recruits(1)(byr,nyr)) - (1.0-rho) * log(rhat(byr,nyr)) - rho * log(++recruits(1)(byr-1,nyr-1)) + sig2R; + break; + } + +// -------------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate prior pdf + * @details Function to calculate prior pdf + * @param pType the type of prior + * @param theta the parameter + * @param p1 the first prior parameter + * @param p2 the second prior parameter + **/ +FUNCTION dvariable get_prior_pdf(const int &pType, const dvariable &_theta, const double &p1, const double &p2) + { + dvariable prior_pdf; + switch(pType) + { + case UNIFORM_PRIOR: // uniform + prior_pdf = dunif(_theta,p1,p2); + break; + case NORMAL_PRIOR: // normal + prior_pdf = dnorm(_theta,p1,p2); + break; + case LOGNORMAL_PRIOR: // lognormal + prior_pdf = dlnorm(_theta,log(p1),p2); + break; + case 3: // beta + prior_pdf = dbeta(_theta,p1,p2); + break; + case 4: // gamma + prior_pdf = dgamma(_theta,p1,p2); + break; + } + return prior_pdf; + } + +// -------------------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate prior density functions for leading parameters. + * @details + * - case 0 is a uniform density between the lower and upper bounds. + * - case 1 is a normal density with mean = p1 and sd = p2 + * - case 2 is a lognormal density with mean = log(p1) and sd = p2 + * - case 3 is a beta density bounded between lb-ub with p1 and p2 as alpha & beta + * - case 4 is a gamma density with parameters p1 and p2. + * + * TODO + * Make this a generic function. + * Agrs would be vector of parameters, and matrix of controls + * @param theta a vector of parameters + * @param C matrix of controls (priorType, p1, p2, lb, ub) + * @return vector of prior densities for each parameter + **/ +FUNCTION calc_prior_densities + double p1,p2; + double lb,ub; + int iprior,itype; + dvariable x; + + // Initialize + priorDensity.initialize(); + + // Key parameter priors + iprior = 1; + for ( int i = 1; i <= ntheta; i++ ) + { + if ( theta_phz(i) > 0 & theta_phz(i) <= current_phase() ) + { + itype = int(theta_control(i,5)); + p1 = theta_control(i,6); + p2 = theta_control(i,7); + dvariable x = theta(i); + if ( itype == 3 ) + { lb = theta_control(i,2); ub = theta_control(i,3); x = (x - lb) / (ub - lb); } + priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2); + iprior += 1; + } + } + + // Growth parameter priors + for ( int i = 1; i <= nGrwth; i++ ) + { + if ( Grwth_phz(i) > 0 & Grwth_phz(i) <= current_phase() ) + { + itype = int(Grwth_control(i,5)); + p1 = Grwth_control(i,6); + p2 = Grwth_control(i,7); + dvariable x = Grwth(i); + if ( itype == 3 ) + { lb = Grwth_control(i,2); ub = Grwth_control(i,3); x = (x - lb) / (ub - lb); } + priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2); + iprior += 1; + } + } + + // Selctivity parameter priors + int j = 1; + for ( int k = 1; k <= nslx; k++ ) + for ( int i = 1; i <= slx_cols(k); i++ ) + { + if ( slx_phzm(j) > 0 & slx_phzm(j) <= current_phase() ) + { + itype = int(slx_priors(k,i,1)); + p1 = slx_priors(k,i,2); + p2 = slx_priors(k,i,3); + if ( slx_type(k) == 0 || slx_type(k) == 1 ) + x = mfexp(log_slx_pars(j)) / (1 + mfexp(log_slx_pars(j))); + else + x = mfexp(log_slx_pars(j)); + // Above is a change of variable so an adjustment is required - DOUBLE CHECK THIS + //priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2) + log_slx_pars(k,j); + priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2); + if ( verbose >= 2 ) cout << " Prior no, val, dens " << iprior << " " << x << " " << priorDensity(iprior) << endl; + iprior++; + } + j++; + } + + // Asymptotic selectivity + for ( int i = 1; i <= NumAsympRet; i++ ) + { + if ( AsympSel_phz(i) > 0 & AsympSel_phz(i) <= current_phase() ) + { + itype = 0; + p1 = AsympSel_lb(i); + p2 = AsympSel_ub(i); + dvariable x = Asymret(i); + priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2); + iprior += 1; + } + } + + // No priors on fbar and foff + for (int i=1; i<=nfleet; i++) if (f_phz(i) > 0 & f_phz(i) <= current_phase()) iprior += 1; + for (int i=1; i<=nfleet; i++) if (f_phz(i) > 0 & f_phz(i) <= current_phase()) iprior += nFparams(i); + for (int i=1; i<=nfleet; i++) if (foff_phz(i) > 0 & foff_phz(i) <= current_phase()) iprior += 1; + for (int i=1; i<=nfleet; i++) if (foff_phz(i) > 0 & foff_phz(i) <= current_phase()) iprior += nYparams(i); + + // no priors on the recruitments (well apart from the later ones) + if (rec_ini_phz > 0 & rdv_phz <= current_phase()) iprior += nclass; + if (rdv_phz > 0 & rdv_phz <= current_phase()) iprior += (rdv_eyr-rdv_syr+1); + if (rec_prop_phz > 0 & rec_prop_phz <= current_phase()) iprior += (rdv_eyr-rdv_syr+1); + + // Natural mortality + for ( int i = 1; i <= nMdev; i++ ) + { + if ( Mdev_phz(i) > 0 & Mdev_phz(i) <= current_phase() ) + { + itype = 0; + p1 = Mdev_lb(i); + p2 = Mdev_ub(i); + dvariable x = m_dev_est(i); + priorDensity(iprior) = get_prior_pdf(itype, x, p1, p2); + iprior += 1; + } + } + + // No priors for effectine sample sizes + for (int i=1;i<=nSizeComps; i++) if (nvn_phz(i) > 0 & nvn_phz(i) <= current_phase()) iprior += 1; + + // Catchability parameter priors + for ( int i = 1; i <= nSurveys; i++ ) + if (q_phz(i) > 0 & q_phz(i) <= current_phase() ) + { + priorDensity(iprior) = get_prior_pdf(prior_qtype(i), survey_q(i), prior_p1(i), prior_p2(i)); + if ( last_phase() ) + priorDensity(iprior) = priorDensity(iprior) ; + else + priorDensity(iprior) = 0.1 * priorDensity(iprior) ; + iprior++; + } + + // Additional CV parameter priors + for ( int i = 1; i <= nSurveys; i++ ) + { + if ( cv_phz(i) > 0 & cv_phz(i) <= current_phase() ) + { + itype = int(prior_add_cv_type(i)); + if (itype == 0) + { + p1 = add_cv_lb(i); + p2 = add_cv_ub(i); + } + else + { + p1 = prior_add_cv_p1(i); + p2 = prior_add_cv_p2(i); + } + priorDensity(iprior) = get_prior_pdf(itype, mfexp(log_add_cv(i)), p1, p2); + iprior += 1; + } + } + + /** + * @brief calculate objective function + * @details + * + * Likelihood components + * -# likelihood of the catch data (assume lognormal error) + * -# likelihood of relative abundance data + * -# likelihood of size composition data + * + * Penalty components + * -# Penalty on log_fdev to ensure they sum to zero. + * -# Penalty to regularize values of log_fbar. + * -# Penalty to constrain random walk in natural mortaliy rates + **/ +FUNCTION calc_objective_function + // |---------------------------------------------------------------------------------| + // | NEGATIVE LOGLIKELIHOOD COMPONENTS FOR THE OBJECTIVE FUNCTION | + // |---------------------------------------------------------------------------------| + dvariable residual, effort, w_nloglike,freq, Prob; + dvariable SumRecF, SumRecM; + + // Reset the likelihood + nloglike.initialize(); + + // 1) Likelihood of the catch data. + for ( int k = 1; k <= nCatchDF; k++ ) + { + for ( int jj = 1; jj <= nCatchRows(k); jj++ ) + { + effort = dCatchData(k,jj,10); ///> Effort data + // In first case (obs_catch > 0) then if there is only catch data, calculate the residual as per usual; if there is catch and effort data, then still use observed catch to calculate the residual, despite the predicted catch being calculated differently. + // In second case, when effort > 0 then the residual is the pred catch using Fs - pred catch using q + if ( obs_catch(k,jj) > 0.0 ) + { + res_catch(k,jj) = log(obs_catch(k,jj)) - log(pre_catch(k,jj)); + } + else + if (effort > 0.0) + { + res_catch(k,jj) = log(obs_catch_effort(k,jj)) - log(pre_catch(k,jj)); + } + } + dvector catch_sd = sqrt(log(1.0 + square(catch_cv(k)))); + nloglike(1,k) += dnorm(res_catch(k), catch_sd); + } + + // 2) Likelihood of the relative abundance data. + for ( int k = 1; k <= nSurveys; k++ ) + { + if ( active(log_add_cv(k)) ) ///> Estimated additional variance + { + for ( int i = 1; i <= nSurveyRows(k); i++ ) + { + dvariable stdtmp = sqrt(log(1.0 + square(cpue_cv(k,i)) + square(mfexp(log_add_cv(k))))); + nloglike(2,k) += log(stdtmp) + 0.5 * square(res_cpue(k,i) / stdtmp); + } + } + else + { + dvector stdtmp = cpue_sd(k) * 1.0 / cpue_lambda(k); ///> Use Sigma scalar instead + dvar_vector restmp = elem_div(log(elem_div(obs_cpue(k), pre_cpue(k))), stdtmp) + 0.5 * stdtmp; + nloglike(2,k) += sum(log(stdtmp)) + sum(0.5 * square(restmp)); + } + } + + // 3) Likelihood for size composition data. + d3_res_size_comps.initialize(); + for ( int ii = 1; ii <= nSizeComps; ii++ ) + { + dmatrix O = d3_obs_size_comps(ii); ///> Observed length frequency + dvar_matrix P = d3_pre_size_comps(ii); ///> Predicted length-frequency + dvar_vector log_effn = log(mfexp(log_vn(ii)) * size_comp_sample_size(ii) * lf_lambda(ii));///> Effective sample size + + bool bCmp = bTailCompression(ii); + class acl::negativeLogLikelihood *ploglike; ///> Negative log-likelihood + + switch ( nAgeCompType(ii) ) ///> Select option for size-comp data + { + case 0: ///> ignore composition data in model fitting. + ploglike = NULL; + break; + case 1: ///> multinomial with fixed or estimated n + ploglike = new class acl::multinomial(O, bCmp); + break; + case 2: ///> robust approximation to the multinomial + ploglike = new class acl::robust_multi(O, bCmp); + break; + case 5: ///> Dirichlet + ploglike = new class acl::dirichlet(O, bCmp); + break; + } + + if ( ploglike != NULL ) ///> extract the residuals + d3_res_size_comps(ii) = ploglike->residual(log_effn, P); + + if ( ploglike != NULL ) ///> Compute the likelihood + { + nloglike(3,ii) += ploglike->nloglike(log_effn, P); + delete ploglike; + } + } + + // 4) Likelihood for recruitment deviations. + dvariable sigR = mfexp(logSigmaR); + nloglike(4,1) = dnorm(res_recruit, sigR); ///> Post first year devs + if (active(rec_ini)) nloglike(4,2) += dnorm(rec_ini, sigR); ///> Initial devs (not used?) + switch ( nSRR_flag ) + { + case 0: ///> Constant recruitment + break; + case 1: + //nloglike(4,1) = dnorm(res_recruit, sigR); ///> Stock-recruitment relationship (not used) + break; + } + if ( active(logit_rec_prop_est) ) ///> Sex-ratio devs + nloglike(4,3) += dnorm(logit_rec_prop_est, 2.0); + + // 5) Likelihood for growth increment data #1 + if (bUseGrowthIncrementModel==LINEAR_GROWTHMODEL & GrowthObsType==GROWTHINC_DATA) + { + dvar_vector MoltIncPred = calc_growth_increments(dPreMoltSize, iMoltIncSex); + nloglike(5,1) = dnorm(log(dMoltInc) - log(MoltIncPred), dMoltIncCV); + } + + // 5) Likelihood for the size-class change data #2 + if (GrowthObsType==GROWTHCLASS_DATA) + { + // First find all FullY matrices + FullY.initialize(); + for (int h = 1; h <=nsex; h++) + for (int k = 1; k<=nSizeIncVaries(h);k++) + { + FullY(h,k,1) = growth_transition(h,k); + for (int itime=2;itime<=MaxGrowTimeLibSex(h);itime++) FullY(h,k,itime) = FullY(h,k,itime-1)*growth_transition(h,k); + } + + for (int i=1;i<=nGrowthObs;i++) + { + int h = iMoltIncSex(i); + int k = iMoltTrans(i); + int iclassRel = iMoltInitSizeClass(i); + int iclassRec = iMoltEndSizeClass(i); + int itimeLib = iMoltTimeAtLib(i); + double freq = float(iMoltSampSize(i)); + Prob = FullY(h,k,itimeLib,iclassRel,iclassRec)+1.0e-20; + nloglike(5,h) += log(Prob)*freq; + } + } // Growth data + + // |---------------------------------------------------------------------------------| + // | PENALTIES AND CONSTRAINTS | + // |---------------------------------------------------------------------------------| + nlogPenalty.initialize(); + + // 1) Penalty on log_fdev (male+combined; female) to ensure they sum to zero + for ( int k = 1; k <= nfleet; k++ ) + { + dvariable s = mean(log_fdev(k)); + nlogPenalty(1) += s*s; + if (nsex > 0) + { + dvariable r = mean(log_fdov(k)); + nlogPenalty(1) += r*r; + } + } + + // 2) Penalty on mean F to regularize the solution. + int irow = 1; + if ( last_phase() ) irow = 2; + dvariable fbar; + dvariable ln_fbar; + for ( int k = 1; k <= nfleet; k++ ) + { + // Jim made penalty apply only to season 2 for Fbar ft(1,nfleet,1,nsex,syr,nyr,1,nseason); ///> Fishing mortality by gear + fbar = mean( trans(ft(k,1))(2) ); + if ( pen_fbar(k) > 0 && fbar != 0 ) + { + ln_fbar = log(fbar); + nlogPenalty(2) += dnorm(ln_fbar, log(pen_fbar(k)), pen_fstd(irow,k)); + } + } + + // 3) Penalty to constrain M in random walk + if (nMdev > 0) + nlogPenalty(3) += dnorm(m_dev_est, m_stdev); + + // 5-6) Penalties on recruitment devs. + if ( !last_phase() ) + { + if ( active(rec_ini) && nSRR_flag !=0 ) nlogPenalty(5) = dnorm(rec_ini, 1.0); + if ( active(rec_dev_est) ) nlogPenalty(6) = dnorm(first_difference(rec_dev), 1.0); + } + + // 7) Penalties on sex-specific recruitment + if (nsex > 1) + { + SumRecF = 0; SumRecM = 0; + for ( int i = syr; i <= nyr; i++ ) + { SumRecF += recruits(2)(i); SumRecM += recruits(1)(i); } + nlogPenalty(7) = square(log(SumRecF) - log(SumRecM)); + } + + w_nloglike = sum(elem_prod(nloglike(1),catch_emphasis)) + sum(elem_prod(nloglike(2),cpue_emphasis)) + sum(elem_prod(nloglike(3),lf_emphasis)); + w_nloglike += sum(nloglike(4)); + objfun = w_nloglike + sum(elem_prod(nlogPenalty,Penalty_emphasis))+ sum(priorDensity) + TempSS; + + // Summary tables + if ( verbose >= 2 ) cout << "Penalties: " << nlogPenalty << endl; + if ( verbose >= 2 ) cout << "Penalties: " << elem_prod(nlogPenalty,Penalty_emphasis) << endl; + if ( verbose >= 2 ) cout << "Likelihoods: " << endl; + if ( verbose >= 2 ) cout << nloglike << endl; + + if ( verbose >= 1 ) cout << "fn Call: " << current_phase() << " " << NfunCall << " " << objfun << " " << w_nloglike << " " << sum(elem_prod(nlogPenalty,Penalty_emphasis)) << " " << sum(priorDensity)+TempSS << endl; + +// ================================================================================================================================================== + + /** + * @brief Simulation model + * @details Uses many of the same routines as the assessment model, over-writes the observed data in memory with simulated data. + **/ +FUNCTION simulation_model + // random number generator + random_number_generator rng(rseed); + + // Initialize model parameters + initialize_model_parameters(); + + // Fishing fleet dynamics ... + calc_selectivities(); + calc_fishing_mortality(); + + dvector drec_dev(syr+1,nyr); + drec_dev.fill_randn(rng); + rec_dev = mfexp(logSigmaR) * drec_dev; + + // Population dynamics ... + calc_growth_increments(); + calc_molting_probability(); + calc_growth_transition(); + calc_natural_mortality(); + calc_total_mortality(); + calc_recruitment_size_distribution(); + calc_initial_numbers_at_length(); + update_population_numbers_at_length(); + + // observation models ... + calc_predicted_catch(); + calc_relative_abundance(); + calc_predicted_composition(); + + // add observation errors to catch. + dmatrix err_catch(1,nCatchDF,1,nCatchRows); + err_catch.fill_randn(rng); + dmatrix catch_sd(1,nCatchDF,1,nCatchRows); + for ( int k = 1; k <= nCatchDF; k++ ) + { + catch_sd(k) = sqrt(log(1.0 + square(catch_cv(k)))); + obs_catch(k) = value(pre_catch(k)); + err_catch(k) = elem_prod(catch_sd(k), err_catch(k)) - 0.5*square(catch_sd(k)); + obs_catch(k) = elem_prod(obs_catch(k), mfexp(err_catch(k))); + for ( int i = syr; i <= nyr; i++ ) + for ( int ii = 1; ii <= nCatchRows(k); ii++ ) + { + if ( dCatchData(k,ii,1) == dCatchData_out(k,i,1) ) // year index + { + obs_catch_out(k,i) = obs_catch(k,ii); + dCatchData_out(k,i,5) = obs_catch(k,ii); + } + } + } + + // add observation errors to cpue & fill in dSurveyData column 5 + dmatrix err_cpue(1,nSurveys,1,nSurveyRows); + dmatrix cpue_sd_sim = sqrt(log(1.0 + square(cpue_cv))); // Note if this should include add_cv + err_cpue.fill_randn(rng); + obs_cpue = value(pre_cpue); + err_cpue = elem_prod(cpue_sd_sim,err_cpue) - 0.5*square(cpue_sd_sim); + obs_cpue = elem_prod(obs_cpue,mfexp(err_cpue)); + for ( int k = 1; k <= nSurveys; k++ ) + for ( int i = 1; i <= nSurveyRows(k); i++ ) + dSurveyData(k,i,5) = obs_cpue(k,i); + + // add sampling errors to size-composition. + double tau; + for ( int k = 1; k <= nSizeComps; k++ ) + for ( int i = 1; i <= nSizeCompRows(k); i++ ) + { + tau = sqrt(1.0 / size_comp_sample_size(k)(i)); + dvector p = value(d3_pre_size_comps(k)(i)); + d3_obs_size_comps(k)(i) = rmvlogistic(p,tau,rseed+k+i); + } + +// ========================================================================================================================================== + + /** + * @brief Calculate mature male biomass (MMB) + * @details Calculation of the mature male biomass is based on the numbers-at-length summed over each shell condition. + * + * TODO: Add female component if lamda < 1 + * + * @return dvar_vector ssb (model mature biomass). + **/ + +FUNCTION dvar_vector calc_ssb() + int ig,h,m,o; + dvar_vector ssb(syr,nyr); + + ssb.initialize(); + for ( int i = syr; i <= nyr; i++ ) + for ( ig = 1; ig <= n_grp; ig++ ) + { + h = isex(ig); + o = ishell(ig); + m = imature(ig); + double lam; + h <= 1 ? lam = spr_lambda : lam = (1.0 - spr_lambda); + ssb(i) += lam * d4_N(ig)(i)(season_ssb) * elem_prod(mean_wt(h)(i), maturity(h)); + } + return(ssb); + +// ====================================================================================================================================== + + // Andre + + /** + * @brief Calculate equilibrium initial conditions + * + * @return dvar_matrix + **/ +FUNCTION dvar_matrix calc_brute_equilibrium(const int YrRef, const int ninit) + int isizeTrans; + double xi; ///> discard mortality rate + dvariable log_ftmp; + dvariable ssb_use; + dvariable TotalRec; + dvar_matrix rtt(1,nsex,1,nclass); ///> constant recruitment + dvar4_array d4_N_init(1,n_grp,1,ninit,1,nseason,1,nclass); ///> N matrix + dvar3_array d4_Npass(1,n_grp,1,nseason,1,nclass); ///> Use to compute reference points + dvar_matrix equilibrium_numbers(1,n_grp,1,nclass); ///> Final numbers-at-size + dvar_vector x(1,nclass); ///> Temp vector for numbers + dvar_vector y(1,nclass); ///> Temp vector for numbers + dvar_vector z(1,nclass); ///> Temp vector for numbers + dvar_matrix MoltProb(1,nsex,1,nclass); ///> Molting probability + dvar3_array _ft(1,nfleet,1,nsex,1,nseason); ///> Fishing mortality by gear (fleet) + dvar3_array FF1(1,nsex,1,nseason,1,nclass); ///> Fishing mortality (continuous) + dvar3_array FF2(1,nsex,1,nseason,1,nclass); ///> Fishing mortality (instantaneous) + dvar3_array ZZ1(1,nsex,1,nseason,1,nclass); ///> Total mortality (continuous) + dvar3_array ZZ2(1,nsex,1,nseason,1,nclass); ///> Total mortality (instananeous) + dvar4_array SS(1,nsex,1,nseason,1,nclass,1,nclass); ///> Surival Rate + dvar_vector ssb_prj(syr,nyr+ninit); ///> projected spawning biomass + dvar_vector hist_ssb(syr,nyr); ///> Historical SSB + dvar_vector Rec_use(1,nsex); ///> Recruitment + dvar_vector sel(1,nclass); ///> Capture selectivity + dvar_vector selret(1,nclass); ///> Selectvity x retained + dvar_vector ret(1,nclass); ///> Retained probability + dvar_vector vul(1,nclass); ///> Total vulnerability + dvar_vector nal(1,nclass); ///> Numbers-at-length + dvar_vector tempZ1(1,nclass); ///> Total mortality + dvar_vector out(1,2+nfleet); + + + // Initialize + d4_N_init.initialize(); + + // copy SSB into projection SSB + hist_ssb = calc_ssb(); + ssb_prj.initialize(); + for (int i = syr; i<=nyr;i++) ssb_prj(i) = hist_ssb(i); + + // set the molt probability (depends on first vs last year) + for (int h = 1; h <= nsex; h++ ) + for (int l=1;l<=nclass;l++) + MoltProb(h,l) = molt_probability(h,YrRef,l); + + // Initialize the Fs + FF1.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + for ( int l = 1; l <= nclass; l++) + FF2(h,j,l) = 1.0e-10; + + // compute the F matrix + for ( int k = 1; k <= nfleet; k++ ) + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + if ( fhit(YrRef,j,k) ) + { + log_ftmp = log_fimpbar(k); + if (h==2) log_ftmp += log_foff(k); + _ft(k,h,j) = mfexp(log_ftmp); + xi = dmr(YrRef,k); // Discard mortality rate + sel = mfexp(log_slx_capture(k,h,YrRef))+1.0e-10; // Selectivity + ret = mfexp(log_slx_retaind(k,h,YrRef)) * slx_nret(h,k); // Retension + vul = elem_prod(sel, ret + (1.0 - ret) * xi); // Vulnerability + FF1(h,j) += _ft(k,h,j) * vul; + FF2(h,j) += _ft(k,h,j) * sel; + } + + // computer the total mortality + ZZ1.initialize(); ZZ2.initialize(); SS.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + { + ZZ1(h,j) = (m_prop(YrRef,j) * M(h,YrRef)) + FF1(h,j); + ZZ2(h,j) = (m_prop(YrRef,j) * M(h,YrRef)) + FF2(h,j); + if (season_type(j) == 0) for ( int l = 1; l <= nclass; l++ ) SS(h,j)(l,l) = 1.0-ZZ1(h,j,l)/ZZ2(h,j,l)*(1.0-mfexp(-ZZ2(h,j,l))); + if (season_type(j) == 1) for ( int l = 1; l <= nclass; l++ ) SS(h,j)(l,l) = mfexp(-ZZ1(h,j,l)); + } + + // recruitment distribution + if (nsex>1) + { + // AEP reset + switch( bSteadyState ) + { + case UNFISHEDEQN: // Unfished conditions + rtt(1) = mfexp(logR0) * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = mfexp(logR0) * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + break; + case FISHEDEQN: // Steady-state fished conditions + rtt(1) = mfexp(logRini) * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = mfexp(logRini) * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + break; + case FREEPARS: // Free parameters + rtt(1) = mfexp(logRbar) * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = mfexp(logRbar) * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + break; + case FREEPARSSCALED: // Free parameters (revised) + rtt(1) = mfexp(logRbar) * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = mfexp(logRbar) * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + break; + case REFPOINTS: // Reference points + rtt(1) = spr_rbar * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = spr_rbar * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + break; + } + } + else + { + switch( bSteadyState ) + { + case UNFISHEDEQN: // Unfished conditions + rtt(1) = mfexp(logR0) * rec_sdd(1); + break; + case FISHEDEQN: // Steady-state fished conditions + rtt(1) = mfexp(logRini) * rec_sdd(1); + break; + case FREEPARS: // Free parameters + rtt(1) = mfexp(logRbar) * rec_sdd(1); + break; + case FREEPARSSCALED: // Free parameters (revised) + rtt(1) = mfexp(logRbar) * rec_sdd(1); + break; + case REFPOINTS: // Reference points + rtt(1) = spr_rbar * rec_sdd(1); + break; + } + } + + // Now project to find the equilibrium + for ( int i = 1; i < ninit; i++ ) + { + // Recruitment + if (Eqn_basis != CONSTANTREC) + { + if (Stock_rec_prj==RICKER) + { + ssb_use = ssb_prj(nyr+i-Age_at_rec_prj); + TotalRec = SR_alpha_prj*ssb_use*exp(-1*SR_beta_prj*ssb_use); + if (nsex>1) + { + rtt(1) = TotalRec * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = TotalRec * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + } + else + rtt(1) = TotalRec * rec_sdd(1); + } + if (Stock_rec_prj==BEVHOLT) + { + ssb_use = ssb_prj(nyr+i-Age_at_rec_prj); + TotalRec = SR_alpha_prj*ssb_use/(SR_beta_prj+ssb_use); + if (nsex>1) + { + rtt(1) = TotalRec * 1 / (1 + mfexp(-logit_rec_prop(YrRef))) * rec_sdd(1); + rtt(2) = TotalRec * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))) * rec_sdd(2); + } + else + rtt(1) = TotalRec * rec_sdd(1); + } + } + + for ( int j = 1; j <= nseason; j++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + isizeTrans = iYrIncChanges(h,YrRef); + int m = imature(ig); + int o = ishell(ig); + x = d4_N_init(ig,i,j); + // Mortality (natural and fishing) + x = x * SS(h,j); + + if ( nshell == 1 ) + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, becomes oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + x += y; + } + // Recruitment + if (j == season_recruitment) x += rtt(h); + if (j == nseason) + d4_N_init(ig)(i+1)(1) = x; + else + d4_N_init(ig)(i)(j+1) = x; + } + else + { + if ( o == 1 ) // newshell + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, becomes oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + } + // Recruitment + if (j == season_recruitment) x += rtt(h); + if (j == nseason) + d4_N_init(ig)(i+1)(1) = x; + else + d4_N_init(ig)(i)(j+1) = x; + } + if ( o == 2 ) // oldshell + { + // Molting and growth + z.initialize(); + if (j == season_growth) + { + z = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, become newshell + x = elem_prod(x, 1 - MoltProb(h)) + y; // did not molt, remain oldshell and add the newshell that become oldshell + } + if (j == nseason) + { d4_N_init(ig-1)(i+1)(1) += z; d4_N_init(ig)(i+1)(1) = x; } + else + { d4_N_init(ig-1)(i)(j+1) += z; d4_N_init(ig)(i)(j+1) = x; } + } + } + } // j + // Project SSB + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + int o = ishell(ig); + int m = imature(ig); + double lam; + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + ssb_prj(nyr+i) += lam * d4_N_init(ig)(i)(season_ssb) * elem_prod(mean_wt(h,YrRef), maturity(h)); + } + } // i + + // Extract the equilibrium numbers (for return) + for ( int ig = 1; ig <= n_grp; ig++ ) + equilibrium_numbers(ig) = d4_N_init(ig)(ninit)(1); + + // ssb to reurn + ssb_pass = ssb_prj(nyr+ninit-1); + + // Projected catche + for ( int j = 1; j <= nseason; j++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + d4_Npass(ig,j) = d4_N_init(ig)(ninit-1)(j); + out = calc_predicted_project(nyr, d4_Npass, _ft, ZZ1, ZZ2); + oflret_pass = out(1); ofltot_pass = out(2); + + return(equilibrium_numbers); + +// ----------------------------------------------------------------------------------------------------------------------------------- + +FUNCTION dvar_vector project_biomass(const int YrRef, const int iproj) + double lam,TACType; + int isizeTrans; + dvariable NF,MMARef,MLARef,StateTAC,TAC2,TargetC; ///> Various temps + dvariable ssb_use,TotalRec,Fmin,Fmax,Fmult; ///> More temps + dvar_vector FederalStuff(1,3); ///> OFLs, ABCs etc + + dvar_matrix rtt(1,nsex,1,nclass); ///> Constant recuitment + dvar_vector MMAState(syr,nyr+iproj); ///> Mature male ABUNDANCE (used in the control rule) + dvar_vector MLAState(syr,nyr+iproj); ///> Legal male ABUNDANCE (used in the control rule) + dvar_vector ssb_prj(syr,nyr+iproj); ///> projected spawning biomass + dvar_vector hist_ssb(syr,nyr); ///> Historical SSB + dvar_vector Rec_use(1,nsex); ///> Recruitment + dvar4_array numbers_proj_gytl(1,n_grp,1,iproj,1,nseason,1,nclass); ///> The N matrix for the projection + dvar4_array d4_PassBack(1,n_grp,1,2,1,nseason,1,nclass); ///> matrix to be returned + dvar_matrix d4_Pass(1,n_grp,1,nclass); ///> Numbers-at-sex/mature/shell/length. + dvar_matrix MoltProb(1,nsex,1,nclass); ///> Molt probability + + // Copy N from the end of the assessment into the first projection year + numbers_proj_gytl.initialize(); + for ( int ig = 1; ig <= n_grp; ig++ ) numbers_proj_gytl(ig)(1)(1) = d4_N(ig)(nyr+1)(1); + + // copy SSB into projection SSB + hist_ssb = calc_ssb(); + ssb_prj.initialize(); + for (int i = syr; i<=nyr;i++) ssb_prj(i) = hist_ssb(i); + + // set the molt probability (depends on first vs last year) + for (int h = 1; h <= nsex; h++ ) + for (int l=1;l<=nclass;l++) + MoltProb(h,l) = molt_probability(h,YrRef,l); + + // recruitment distribution (constant recruitment) + if (nsex>1) + { + rtt(1) = spr_rbar / 2.0 * rec_sdd(1); + rtt(2) = spr_rbar / 2.0 * rec_sdd(2); + } + else + rtt(1) = spr_rbar * rec_sdd(1); + + // Compute MMA and MMB at the start of the season (needed for the State HCRs) + MMAState.initialize(); MLAState.initialize(); + for ( int i = syr; i <= nyr; i++ ) + { + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + isizeTrans = iYrIncChanges(h,YrRef); + int m = imature(ig); + int o = ishell(ig); + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + MMAState(i) += sum(lam * elem_prod(d4_N(ig,i,1), maturity(h))); + MLAState(i) += sum(lam * elem_prod(d4_N(ig,i,1), legal(h))); + } + } + MMARef = 0; MLARef = 0; NF = 0; + for ( int i = 1978; i <= 2012; i++ ) { MMARef += MMAState(i); MLARef += MLAState(i); NF += 1; } + MMARef /= NF; MLARef /= NF; + + + // Now project forward + for ( int i = 1; i <= iproj; i++ ) + { + if (Stock_rec_prj==1) // Constant recruitment + { + for (int h=1; h<=nsex;h++) Rec_use(h) = fut_recruits(h,i); + } + if (Stock_rec_prj==RICKER) // Ricker + { + ssb_use = ssb_prj(nyr+i-Age_at_rec_prj); + TotalRec = SR_alpha_prj*ssb_use*exp(-1*SR_beta_prj*ssb_use)*fut_recruits(1,i); + if (nsex>1) + { + Rec_use(1) = TotalRec * 1 / (1 + mfexp(-logit_rec_prop(YrRef))); + Rec_use(2) = TotalRec * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))); + } + else + Rec_use(1) = TotalRec; + } + if (Stock_rec_prj==BEVHOLT) // Beveton-Holt + { + ssb_use = ssb_prj(nyr+i-Age_at_rec_prj); + TotalRec = SR_alpha_prj*ssb_use/(SR_beta_prj+ssb_use); + if (nsex>1) + { + Rec_use(1) = TotalRec * 1 / (1 + mfexp(-logit_rec_prop(YrRef))); + Rec_use(2) = TotalRec * (1 - 1 / (1 + mfexp(-logit_rec_prop(YrRef)))); + } + else + Rec_use(1) = TotalRec; + } + + // Store start-year N-at-size + d4_Pass.initialize(); + for ( int ig = 1; ig <= n_grp; ig++ ) d4_Pass(ig) = numbers_proj_gytl(ig,i,1); + + // Compute the ABC and OFL + FederalStuff = compute_OFL_and_ABC(nyr+i, d4_Pass); + //cout << "Feds " << nyr+i << " " << FederalStuff << endl; + + // Compute MMA and MLA at the start of the season (needed for the State HCRs) + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + isizeTrans = iYrIncChanges(h,YrRef); + int m = imature(ig); + int o = ishell(ig); + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + MMAState(nyr+i) += sum(lam * elem_prod(numbers_proj_gytl(ig,i,1), maturity(h))); + MLAState(nyr+i) += sum(lam * elem_prod(numbers_proj_gytl(ig,i,1), legal(h))); + } + + // Compute the TAC + //cout << "STATE " << MMAState(nyr+i)*MeanWState << " " << MLAState(nyr+i)*MeanWState << " " << MMAState(nyr+i)/MMARef << endl; + if (MMAState(nyr+i) < 0.5*MMARef) + StateTAC = 0; + else + if (MMAState(nyr+i) >= 0.5*MMARef & MMAState(nyr+i) < MMARef) + { + StateTAC = 0.1*(MMAState(nyr+i)/MMARef)*MMAState(nyr+i)*MeanWStateMature; + } + else + { + StateTAC = 0.1*MMAState(nyr+i)*MeanWStateMature; + } + TAC2 = 0.25*MLAState(nyr+i)*MeanWStateLegal; + if (TAC2 < StateTAC) StateTAC = TAC2; + + // Adjust so that the TAC is not larger than the ABC + if (FederalStuff(3) < StateTAC) + { TACType = 1; TargetC = FederalStuff(2); } /// > Hit the total catch OFL + else + { TACType = 2; TargetC = StateTAC; } /// > Hit the retained catc OFL + if (Apply_HCR_prj==1) cout << "TACS " << nyr+i << " Total ABC " << FederalStuff(2) << " Retained ABC " << FederalStuff(3) << " StateTAC " << StateTAC << " Final Decision: " << TACType << " " << TargetC << endl; + + // DO a projection for the current F + log_fimpbarPass = log_fimpbar; + d4_PassBack = project_one_year(i, iproj, YrRef, MoltProb, rtt, rec_sdd, Rec_use, d4_Pass); + + int NeedToTune; + if (Apply_HCR_prj==1) NeedToTune = YES; + if (Apply_HCR_prj==0) NeedToTune = NO; + if (TACType==1 && catch_pass(2)-1.0e-5 < TargetC) NeedToTune = NO; + if (TACType==2 && catch_pass(1)-1.0e-5 < TargetC) NeedToTune = NO; + + // Apply bisection to find the target F for the directed fishery + if (NeedToTune==YES) + { + Fmin = 0; Fmax =1; + for (int ii=1; ii<=20;ii++) + { + Fmult = (Fmin+Fmax)/2.0; + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbarPass(k) = log(mfexp(log_fimpbar(k))*Fmult); + d4_PassBack = project_one_year(i, iproj, YrRef, MoltProb, rtt, rec_sdd, Rec_use, d4_Pass); + if (TACType == 1) + { + if (catch_pass(2) > TargetC) Fmax = Fmult; else Fmin = Fmult; + } + if (TACType == 2) + { + if (catch_pass(1) > TargetC) Fmax = Fmult; else Fmin = Fmult; + } + } + cout << "CTUNE " << Fmult << " " << TargetC << " " << catch_pass << endl; + } + + // Restore the N-matrix + for ( int ig = 1; ig <= n_grp; ig++ ) + for ( int j = 2; j <= nseason; j++) + numbers_proj_gytl(ig,i,j) = d4_PassBack(ig,1,j); + if (i != iproj) + for ( int ig = 1; ig <= n_grp; ig++ ) + numbers_proj_gytl(ig,i+1,1) = d4_PassBack(ig,2,1); + ssb_prj(nyr+i) = ssb_pass; + + } // i + + // return + ssb_pass = ssb_prj(nyr+iproj); + + return(ssb_prj); + +// ----------------------------------------------------------------------------------------------------------------------------------- + +FUNCTION dvar4_array project_one_year(const int i, const int iproj, const int YrRef, dvar_matrix MoltProb, + dvar_matrix rtt, dvar_matrix rec_sdd, dvar_vector Rec_use, dvar_matrix d4_Pass) + { + int isizeTrans; + double xi; ///> discard mortality rate + dvariable log_ftmp; + dvar4_array numbers_proj_gytl(1,n_grp,1,2,1,nseason,1,nclass); ///> Numbers-at-size + dvar_vector x(1,nclass); ///> Temp vector for numbers + dvar_vector y(1,nclass); ///> Temp vector for numbers + dvar_vector z(1,nclass); ///> Temp vector for numbers + dvar3_array _F1(1,nsex,1,nseason,1,nclass); ///> Fishing mortality + dvar3_array _F2(1,nsex,1,nseason,1,nclass); ///> Fishing mortality + dvar3_array _Z1(1,nsex,1,nseason,1,nclass); ///> Total mortality + dvar3_array _Z2(1,nsex,1,nseason,1,nclass); ///> Total mortality + dvar4_array _S(1,nsex,1,nseason,1,nclass,1,nclass); ///> Surival Rate (S=exp(-Z)) + dvar3_array _ft(1,nfleet,1,nsex,1,nseason); ///> Fishing mortality by gear + dvar3_array d4_Npass(1,n_grp,1,nseason,1,nclass); ///> For passing out + dvar_vector sel(1,nclass); ///> Capture selectivity + dvar_vector selret(1,nclass); ///> Selectvity x retained + dvar_vector ret(1,nclass); ///> Retained probability + dvar_vector vul(1,nclass); ///> Total vulnerability + dvar_vector nal(1,nclass); ///> Numbers-at-length + + // Pass in + numbers_proj_gytl.initialize(); + for ( int ig = 1; ig <= n_grp; ig++ ) numbers_proj_gytl(ig,1,1) = d4_Pass(ig); + + // Initialize the Fs + _F1.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + for ( int l = 1; l <= nclass; l++) + _F2(h,j,l) = 1.0e-10; + + _ft.initialize(); + for ( int k = 1; k <= nfleet; k++ ) + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + if ( fhit(YrRef,j,k) ) + { + log_ftmp = log_fimpbarPass(k); + if (h==2) log_ftmp += log_foff(k); + _ft(k,h,j) = mfexp(log_ftmp); + xi = dmr(YrRef,k); // Discard mortality rate + sel = mfexp(log_slx_capture(k,h,YrRef))+1.0e-10; // Selectivity + ret = mfexp(log_slx_retaind(k,h,YrRef)) * slx_nret(h,k); // Retension + vul = elem_prod(sel, ret + (1.0 - ret) * xi); // Vulnerability + _F1(h,j) += _ft(k,h,j) * vul; + _F2(h,j) += _ft(k,h,j) * sel; + } + + // computer the total mortality + _Z1.initialize(); _Z2.initialize(); _S.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + { + _Z1(h,j) = (m_prop(YrRef,j) * M(h,YrRef)) + _F1(h,j); + _Z2(h,j) = (m_prop(YrRef,j) * M(h,YrRef)) + _F2(h,j); + if (season_type(j) == 0) for ( int l = 1; l <= nclass; l++ ) _S(h,j)(l,l) = 1.0-_Z1(h,j,l)/_Z2(h,j,l)*(1.0-mfexp(-_Z2(h,j,l))); + if (season_type(j) == 1) for ( int l = 1; l <= nclass; l++ ) _S(h,j)(l,l) = mfexp(-_Z1(h,j,l)); + } + + // Update the dynamics + for ( int j = 1; j <= nseason; j++ ) + { + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + isizeTrans = iYrIncChanges(h,YrRef); + int m = imature(ig); + int o = ishell(ig); + x = numbers_proj_gytl(ig,1,j); + // Mortality (natural and fishing) + x = x * _S(h,j); + + if ( nshell == 1 ) + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, become oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + x += y; + } + // Recruitment + if (j == season_recruitment) + { + if (IsProject==NOPROJECT) x += rtt(h); + if (IsProject==PROJECT) x += Rec_use(h)*rec_sdd(h); + } + if (j == nseason) + { + if (i != iproj) numbers_proj_gytl(ig,2,1) = x; + } + else + numbers_proj_gytl(ig,1,j+1) = x; + } + else + { + if ( o == 1 ) // newshell + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, become oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + } + // Recruitment + if (j == season_recruitment) + { + if (IsProject==NOPROJECT) x += rtt(h); + if (IsProject==PROJECT) x += Rec_use(h)*rec_sdd(h); + } + if (j == nseason) + { + if (i != iproj) + numbers_proj_gytl(ig,2,1) = x; + } + else + numbers_proj_gytl(ig,1,j+1) = x; + } + if ( o == 2 ) // oldshell + { + // Molting and growth + if (j == season_growth) + { + z = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, become newshell + x = elem_prod(x, 1 - MoltProb(h)) + y; // did not molt, remain oldshell and add the newshell that become oldshell + } + if (j == nseason) + { + if (i != iproj) + { numbers_proj_gytl(ig-1,2,1) += z; numbers_proj_gytl(ig,2,1) = x; } + } + else + { numbers_proj_gytl(ig-1,1,j+1) += z; numbers_proj_gytl(ig,1,j+1) = x; } + } // oldshell + } // nshell = 2 + } // ig + } // j + + // Project SSB + ssb_pass = 0; + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + int o = ishell(ig); + int m = imature(ig); + double lam; + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + ssb_pass += lam * numbers_proj_gytl(ig,1,season_ssb) * elem_prod(mean_wt(h,YrRef), maturity(h)); + } + + for ( int j = 1; j <= nseason; j++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + d4_Npass(ig,j) = numbers_proj_gytl(ig,1,j); + catch_pass = calc_predicted_project(nyr, d4_Npass, _ft, _Z1, _Z2); + + return(numbers_proj_gytl); + } + +// ----------------------------------------------------------------------------------------------------------------------------------- + +FUNCTION dvar_vector calc_predicted_project(const int YrRef, dvar3_array d4_Npass, dvar3_array _ft, dvar3_array _Z1, dvar3_array _Z2) + { + //int h,i,j,k,ig,type,unit; + double xi; ///> Discard rate + dvar_vector out(1,2+nfleet); ///> Output + dvariable tmp_ft,out2; ///> Temp + dvar_vector sel(1,nclass); ///> Capture selectivity + dvar_vector selret(1,nclass); ///> Selectvity x retained + dvar_vector ret(1,nclass); ///> Retained probability + dvar_vector vul(1,nclass); ///> Total vulnerability + dvar_vector nal(1,nclass); ///> Numbers-at-length + dvar_vector tempZ1(1,nclass); ///> total mortality + + // out(1): retained catch + // out(2): dead total catch + // out(2+k): dead animals + + out.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + { + nal.initialize(); + for ( int m = 1; m <= nmature; m++ ) + for ( int o = 1; o <= nshell; o++ ) + { int ig = pntr_hmo(h,m,o); nal += d4_Npass(ig)(j); } + nal = elem_prod(nal, mean_wt(h)(YrRef)); + for ( int k = 1; k <= nfleet; k++ ) + { + xi = dmr(YrRef,k); ///> Discard mortality rate + sel = mfexp(log_slx_capture(k,h,YrRef))+1.0e-10; ///> Selectivity + ret = mfexp(log_slx_retaind(k,h,YrRef)) * slx_nret(h,k); ///> Retension + vul = elem_prod(sel, ret + (1.0 - ret) * xi); ///> Vulnerability + selret = elem_prod(sel,ret); + if (season_type(j)==0) tempZ1 = _Z1(h,j); else tempZ1 = _Z2(h,j); + out(1) += nal * elem_div(elem_prod(_ft(k,h,j) * selret, 1.0 - mfexp(-tempZ1)), tempZ1); + // Total dead + out(2) += nal * elem_div(elem_prod(_ft(k,h,j) * vul, 1.0 - mfexp(-tempZ1)), tempZ1); + // fleet-specific dead crab + out(2+k) += nal * elem_div(elem_prod(_ft(k,h,j) * vul, 1.0 - mfexp(-tempZ1)), tempZ1); + } + } + return(out); + } + +// --------------------------------------------------------------------------------------------------------------------------------------- +FUNCTION dvar_vector compute_OFL_and_ABC(const int iyr, dvar_matrix d4_Npass) + { + int IsProjectSave; ///> Variable to save the projection pointer + dvar_vector Bproj(1,1); ///> Projected biomass (one year) + dvar_vector return_vec(1,3); ///> Material to return + dvariable Fmsy, Bmsy, Fmult2, FF; ///> Teps + + IsProjectSave = IsProject; + IsProject = NOPROJECT; + + // Extract reference points + Fmsy = 1; Bmsy = spr_bmsy; + + // Set F to Fmsy (by fleet) + log_fimpbarOFL = log(sd_fmsy); + Bproj = project_biomass_OFL(nyr, 1,d4_Npass); + + if (ssb_pass < Bmsy & ssb_pass >= OFLbeta * Bmsy) + { + // Adjust F if below target since it's a function biomass needs to be interated + for( int iloop = 1; iloop <= 10; iloop++) + { + Fmult2 = Fmsy * (ssb_pass / Bmsy - OFLalpha) / (1 - OFLalpha); + if (Fmult2 < 0.1*FF) + FF = 0.1*FF; + else + FF = Fmult2; + if (FF < 0.00001) FF = 0.00001; + //cout << "TuneB " << Fmsy << " " << FF << " " << ssb_pass << " " << ssb_pass/Bmsy << " " << ssb_pass/ssbF0 < Return back to normal + + return(return_vec); + } + +// ----------------------------------------------------------------------------------------------------------------------------------- + +FUNCTION dvar_vector project_biomass_OFL(const int YrRef, const int iproj, dvar_matrix d4_Npass) + int isizeTrans; ///> Size-transition matrix + dvariable log_ftmp; ///> Temp + double xi; ///> Discard mortality + dvar_matrix rtt(1,nsex,1,nclass); ///> Constant recruitment + dvar_vector x(1,nclass); ///> Temp vector for numbers + dvar_vector y(1,nclass); ///> Temp vector for numbers + dvar_vector z(1,nclass); ///> Temp vector for numbers + dvar3_array _F1(1,nsex,1,nseason,1,nclass); ///> Fishing mortality + dvar3_array _F2(1,nsex,1,nseason,1,nclass); ///> Fishing mortality + dvar3_array _Z1(1,nsex,1,nseason,1,nclass); ///> Total mortality + dvar3_array _Z2(1,nsex,1,nseason,1,nclass); ///> Total mortality + dvar4_array _S(1,nsex,1,nseason,1,nclass,1,nclass); ///> Surival Rate (S=exp(-Z)) + dvar3_array _ft(1,nfleet,1,nsex,1,nseason); ///> Fishing mortality by gear + dvar4_array numbers_proj_gytl(1,n_grp,1,iproj,1,nseason,1,nclass); ///> N matrix + dvar3_array d4_Npass_2(1,n_grp,1,nseason,1,nclass); ///> Pass variable + dvar_vector sel(1,nclass); ///> Capture selectivity + dvar_vector ret(1,nclass); ///> Retained probability + dvar_vector vul(1,nclass); ///> Total vulnerability + dvar_vector nal(1,nclass); ///> Numbers-at-length + dvar_vector out(1,2+nfleet); ///> Output + + // Copy N from the end of the assessment into the first projection year + numbers_proj_gytl.initialize(); + for ( int ig = 1; ig <= n_grp; ig++ ) + numbers_proj_gytl(ig)(1)(1) = d4_Npass(ig); + + dvar_matrix MoltProb(1,nsex,1,nclass); + for (int h = 1; h <= nsex; h++ ) + for (int l=1;l<=nclass;l++) + MoltProb(h,l) = molt_probability(h,YrRef,l); + + // recruitment distribution + if (nsex>1) + { + rtt(1) = spr_rbar / 2.0 * rec_sdd(1); + rtt(2) = spr_rbar / 2.0 * rec_sdd(2); + } + else + rtt(1) = spr_rbar * rec_sdd(1); + + // Initialize the Fs + _F1.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + for ( int l = 1; l <= nclass; l++) + _F2(h,j,l) = 1.0e-10; + + _ft.initialize(); + for ( int k = 1; k <= nfleet; k++ ) + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + if ( fhit(YrRef,j,k) ) + { + log_ftmp = log_fimpbarOFL(k); + if (h==2) log_ftmp += log_foff(k); + _ft(k,h,j) = mfexp(log_ftmp); + xi = dmr(YrRef,k); // Discard mortality rate + sel = mfexp(log_slx_capture(k,h,YrRef))+1.0e-10; // Selectivity + ret = mfexp(log_slx_retaind(k,h,YrRef)) * slx_nret(h,k); // Retension + vul = elem_prod(sel, ret + (1.0 - ret) * xi); // Vulnerability + _F1(h,j) += _ft(k,h,j) * vul; + _F2(h,j) += _ft(k,h,j) * sel; + } + + // computer the total mortality + _Z1.initialize(); _Z2.initialize(); _S.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int j = 1; j <= nseason; j++ ) + { + _Z1(h)(j) = (m_prop(YrRef,j) * M(h,YrRef)) + _F1(h,j); + _Z2(h)(j) = (m_prop(YrRef,j) * M(h,YrRef)) + _F2(h,j); + if (season_type(j) == 0) for ( int l = 1; l <= nclass; l++ ) _S(h,j)(l,l) = 1.0-_Z1(h,j,l)/_Z2(h,j,l)*(1.0-mfexp(-_Z2(h,j,l))); + if (season_type(j) == 1) for ( int l = 1; l <= nclass; l++ ) _S(h,j)(l,l) = mfexp(-_Z1(h,j,l)); + } + + // Now project(usually for 1 year) + for ( int i = 1; i <= iproj; i++ ) + { + + for ( int j = 1; j <= nseason; j++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + isizeTrans = iYrIncChanges(h,YrRef); + int m = imature(ig); + int o = ishell(ig); + x = numbers_proj_gytl(ig,i,j); + // Mortality (natural and fishing) + x = x * _S(h,j); + + if ( nshell == 1 ) + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, become oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + x += y; + } + // Recruitment + if (j == season_recruitment) x += rtt(h); + if (j == nseason) + { + if (i != iproj) numbers_proj_gytl(ig)(i+1)(1) = x; + } + else + numbers_proj_gytl(ig)(i)(j+1) = x; + } + else + { + if ( o == 1 ) // newshell + { + // Molting and growth + if (j == season_growth) + { + y = elem_prod(x, 1 - MoltProb(h)); // did not molt, become oldshell + x = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, stay newshell + } + // Recruitment + if (j == season_recruitment) x += rtt(h); + if (j == nseason) + { + if (i != iproj) + numbers_proj_gytl(ig)(i+1)(1) = x; + } + else + numbers_proj_gytl(ig)(i)(j+1) = x; + } + if ( o == 2 ) // oldshell + { + // Molting and growth + z.initialize(); + if (j == season_growth) + { + z = elem_prod(x, MoltProb(h)) * growth_transition(h,isizeTrans); // molted and grew, become newshell + x = elem_prod(x, 1 - MoltProb(h)) + y; // did not molt, remain oldshell and add the newshell that become oldshell + } + if (j == nseason) + { + if (i != iproj) + { numbers_proj_gytl(ig-1)(i+1)(1) += z; numbers_proj_gytl(ig)(i+1)(1) = x; } + } + else + { numbers_proj_gytl(ig-1)(i)(j+1) += z; numbers_proj_gytl(ig)(i)(j+1) = x; } + } // oldshell + } // nshell = 2 + } // j + + } // i + + dvar_vector ssb(1,iproj); + ssb.initialize(); + for ( int i = 1; i <= iproj; i++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + { + int h = isex(ig); + int o = ishell(ig); + int m = imature(ig); + double lam; + h <= 1 ? lam = spr_lambda: lam = (1.0 - spr_lambda); + ssb(i) += lam * numbers_proj_gytl(ig)(i)(season_ssb) * elem_prod(mean_wt(h)(YrRef), maturity(h)); + } + + // return material + ssb_pass = ssb(iproj); + for ( int j = 1; j <= nseason; j++ ) + for ( int ig = 1; ig <= n_grp; ig++ ) + d4_Npass_2(ig,j) = numbers_proj_gytl(ig)(iproj)(j); + out = calc_predicted_project(nyr, d4_Npass_2, _ft, _Z1, _Z2); + oflret_pass = out(1); ofltot_pass = out(2); + + return(ssb); + +// ---------------------------------------------------------------------------------------------------------------------------------------- + + +FUNCTION void calc_spr_reference_points2(const int DoProfile) + int iproj; + dvar_matrix equilibrium_numbers(1,n_grp,1,nclass); + dvariable FF; + dvariable Fmsy; + dvariable MeanF,NF; + dvariable Fmult, Fmult2, SSBV0, SSBV1a,SSBV1b,Deriv, Adjust, R1; + dvariable SteepMin,SteepMax; + dvar_vector Fave(1,nfleet); + dvar_matrix d4_Npass(1,n_grp,1,nclass); ///> Numbers-at-sex/mature/shell/length. + dvar_vector tst(1,3); + + bSteadyState = REFPOINTS; + IsProject = NOPROJECT; + iproj = 1; + dvar_vector Bproj(1,iproj); + Eqn_basis = CONSTANTREC; + + // Find mean recruitment + if (nsex==1) spr_rbar = mean(recruits(1)(spr_syr,spr_nyr)); + if (nsex==2) spr_rbar = mean(recruits(1)(spr_syr,spr_nyr))+mean(recruits(2)(spr_syr,spr_nyr)); + + // Find the average F by fleet over the last 4 years + for (int k = 1; k <= nfleet; k++) + { + MeanF = 0; NF = 0; + for (int i = spr_aveF_syr; i <= spr_aveF_nyr; i++) { MeanF += fout(k,i); NF += 1; } + Fave(k) = (MeanF+1.0e-10)/NF; + } + + // Find SSB for F=0 for all fleets + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = -100; + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + ssbF0 = ssb_pass; + + // Solve for F35% and hence the Fmsy proxy (Tier 3 analysis) + if (OFLTier==3) + { + + // Find Fmsy (=F35%) when adjusting the Fs for some fleets + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = log(Fave(k)); + Fmult = 1.0; + for (int i=1;i<=10;i++) + { + // Set F + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fave(k)*Fmult); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV0 = ssb_pass/ssbF0-spr_target; + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fave(k)*(Fmult+0.001)); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1a = ssb_pass/ssbF0-spr_target; + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fave(k)*(Fmult-0.001)); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1b = ssb_pass/ssbF0-spr_target; + Deriv = (SSBV1a-SSBV1b)/0.002; + Fmult2 = Fmult - SSBV0/Deriv; + if (Fmult2 < 0.01) Fmult2 = 0.01; + if (Fmult2 < 0.1*Fmult) + Fmult = 0.1*Fmult; + else + Fmult = Fmult2; + } + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fave(k)*Fmult); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + Bmsy = ssb_pass; + } + + // Tier 4 control rule + if (OFLTier==4) + { + // BMSY is the mean SSB over a set of years + Bmsy = mean(calc_ssb()(spr_syr,spr_nyr)); + Fmsy = OFLgamma * M0(1); + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = log(Fave(k)); + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fmsy); + } + + // Save FMSY + for (int k=1;k<=nfleet;k++) sd_fmsy(k) = mfexp(log_fimpbar(k)); + + // Store reference points + Fmsy = 1; + spr_bmsy = Bmsy; + + // Store the N from the last year + for ( int ig = 1; ig <= n_grp; ig++ ) d4_Npass(ig) = d4_N(ig)(nyr+1)(1); + + // Base projections on last-year biological and fishery parameters + log_fimpbarOFL = log(sd_fmsy); + Bproj = project_biomass_OFL(nyr, iproj,d4_Npass); + spr_fofl = 1.0; + spr_depl = ssb_pass / Bmsy; + + if (ssb_pass < Bmsy & ssb_pass >= OFLbeta * Bmsy) + { + // Adjust F if below target since it's a function biomass needs to be interated + for( int iloop = 1; iloop <= 10; iloop++) + { + Fmult2 = Fmsy * (ssb_pass / Bmsy - OFLalpha) / (1 - OFLalpha); + if (Fmult2 < 0.1*FF) + FF = 0.1*FF; + else + FF = Fmult2; + if (FF < 0.00001) FF = 0.00001; + //cout << "TuneA " << Fmsy << " " << FF << " " << ssb_pass << " " << ssb_pass/Bmsy << " " << ssb_pass/ssbF0 < 0) + { + SteepMin = Steepness - (SteepMax-SteepMin)/29.0; + SteepMax = Steepness + (SteepMax-SteepMin)/29.0; + ii = 40; + } + } + + + //SteepMin = 0.2; SteepMax = 5.0; + for (int ii=1;ii<=30;ii++) + { + Steepness = (SteepMin+SteepMax)/2.0; + if (Stock_rec_prj==RICKER) + { + SR_alpha_prj = spr_rbar/ssbF0*exp(log(5.0*Steepness)/0.8); + SR_beta_prj = log(5.0*Steepness)/(0.8*ssbF0); + } + if (Stock_rec_prj==BEVHOLT) + { + SR_alpha_prj = 4.0*Steepness*spr_rbar/(5*Steepness-1.0); + SR_beta_prj = (1.0-Steepness)*ssbF0/(5*Steepness-1.0); + } + + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(sd_fmsy(k)*1.001); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1a = oflret_pass; + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(sd_fmsy(k)*0.999); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1b = oflret_pass; + Deriv = (SSBV1a-SSBV1b)/0.002; + if (Deriv < 0) SteepMin = Steepness; else SteepMax = Steepness; + } + cout << Steepness << " " << Deriv << " " << ssbF0 << endl; + if (Stock_rec_prj==RICKER) + { + SR_alpha_prj = spr_rbar/ssbF0*exp(log(5.0*Steepness)/0.8); + SR_beta_prj = log(5.0*Steepness)/(0.8*ssbF0); + } + if (Stock_rec_prj==BEVHOLT) + { + SR_alpha_prj = 4.0*Steepness*spr_rbar/(5*Steepness-1.0); + SR_beta_prj = (1.0-Steepness)*ssbF0/(5*Steepness-1.0); + } + log_fimpbar = log(sd_fmsy); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + + Adjust = spr_bmsy/ssb_pass; + if (Stock_rec_prj==RICKER) + { + SR_alpha_prj = spr_rbar/ssbF0*exp(log(5.0*Steepness)/0.8); + SR_beta_prj = log(5.0*Steepness)/(0.8*ssbF0*Adjust); + } + if (Stock_rec_prj==BEVHOLT) + { + SR_alpha_prj = 4.0*Steepness*spr_rbar*Adjust/(5*Steepness-1.0); + SR_beta_prj = (1.0-Steepness)*ssbF0*Adjust/(5*Steepness-1.0); + } + + // Find SSB for F=0 for all fleets + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = -100; + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + ssbF0 = ssb_pass; + + // Check derivative + log_fimpbar = log(sd_fmsy); + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(sd_fmsy(k)*1.001); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1a = oflret_pass; + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(sd_fmsy(k)*0.999); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + SSBV1b = oflret_pass; + Deriv = (SSBV1a-SSBV1b)/0.002; + + log_fimpbar = log(sd_fmsy); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + cout << ssb_pass/ssbF0 << " " << ssb_pass/spr_bmsy << " " << exp(log_fimpbar) << " " << oflret_pass << " " << Steepness << " " << Deriv << " " << ssbF0 << " " << spr_bmsy << endl; + + if (Compute_yield_prj==YES & DoProfile==YES) + for (int ii=0;ii<=100;ii++) + { + if (ii==0) FF = 1.0e-10; else FF = float(ii)/20; + for (int k=1;k<=nfleet;k++) if (Ffixed(k) != 1) log_fimpbar(k) = log(sd_fmsy(k)*FF); + equilibrium_numbers = calc_brute_equilibrium(nyr,200); + spr_yield(ii,1) = ssb_pass/ssbF0; + spr_yield(ii,2) = ssb_pass/spr_bmsy; + spr_yield(ii,3) = exp(log_fimpbar(1)); + spr_yield(ii,4) = oflret_pass; + } + } +// ======================================================================================================================================= + + /** + * @brief Conduct projections + **/ + +// andre + +FUNCTION write_eval + int index; ///> Counters + dvariable MeanF,NF,Fmult,Bmsy_out; ///> Temp variables + dvar_vector Bproj(syr,nyr+nproj); ///> Biomass outout + dvar_vector Fave(1,nfleet); ///> Average F + + // Darcy MCMC output + MCout(theta); + + // Header + if (NfunCall==1) + { + } + + /** + * @brief calculate sd_report variables in final phase + **/ + calc_spr_reference_points2(0); + + mcoutSSB << calc_ssb() << endl; + if (nsex==1) mcoutREC << recruits(1) << endl; + if (nsex==2) mcoutREC << recruits(1)+recruits(2) << endl; + mcoutREF << NfunCall << " " << spr_rbar << " " << ssbF0 << " " << spr_bmsy << " " << spr_depl << " " << spr_cofl << " " << sd_fmsy << " " << sd_fofl << endl; + + // Find the average F by fleet over the last 4 years + for (int k = 1; k <= nfleet; k++) + { + MeanF = 0; NF = 0; + for (int i = spr_aveF_syr; i <= spr_aveF_nyr; i++) { MeanF += fout(k,i); NF += 1; } + Fave(k) = (MeanF+1.0e-10)/NF; + } + + // Set the average Fs for the non-adjusted fleets + if (prj_bycatch_on==NO) + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = log(1.0e-10); + else + for (int k=1;k<=nfleet;k++) log_fimpbar(k) = log(Fave(k)); + + // Should Bmsy be simulatation-specific or a pre-specified value + if (Fixed_prj_Bmsy < 0) Bmsy_out = spr_bmsy; else Bmsy_out = Fixed_prj_Bmsy; + + // Do projections + IsProject = PROJECT; + if (prj_Nstrat > 0 & prj_replicates > 0) + for (int isim=1;isim<=prj_replicates;isim++) + { + // generate future recruitment + for (int iproj=1;iproj<=nproj;iproj++) + { + if (Stock_rec_prj==UNIFORMSR) + { + index = prj_futRec_syr+(int)floor((float(prj_futRec_nyr)-float(prj_futRec_syr)+1.0)*randu(rng)); + fut_recruits(1,iproj) = recruits(1,index); + if (nsex==2) fut_recruits(2,iproj) = recruits(2,index); + } + if (Stock_rec_prj==RICKER || Stock_rec_prj==BEVHOLT) + { + fut_recruits(1,iproj) = mfexp(randn(rng)*SigmaR_prj-SigmaR_prj*SigmaR_prj/2.0); + if (nsex==2) fut_recruits(2,iproj) = fut_recruits(1,iproj); + } + } + for (int irun=1;irun<=prj_Nstrat;irun++) + { + // Set F + Fmult = 1.e-10 + prj_lowF + float(irun-1)*(prj_hiF-prj_lowF)/float(prj_Nstrat-1); + for (int k=1;k<=nfleet;k++) + if (Ffixed(k) != 1) log_fimpbar(k) = log(Fmult); + + Bproj = project_biomass(nyr, nproj); + mcoutPROJ << NfunCall << " " << isim << " " << irun << " " << exp(log_fimpbar) << " " << Bmsy_out << " "; + for (int i=nyr+1;i<=nyr+nproj;i++) mcoutPROJ << Bproj(i) << " "; + cout << Bproj(nyr+nproj-1) << endl; + mcoutPROJ < Fishing mortality + dvar4_array ftmp2(1,nsex,syr,nyr,1,nseason,1,nclass); ///> Fishing mortality + + // save the fishing mortality rates + ftmp = F; ftmp2 = F2; + + // standard deviations of assessment outcomes + sd_log_recruits = log(recruits); + sd_log_ssb = log(calc_ssb()); + //Added 13 lines by Jie + sd_last_ssb = spr_depl * Bmsy; + for (int i=syr; i<= nyr-7; i++) + // for (int i=syr+1; i<= nyr; i++) + { + sdrRec(i) = recruits(1,i+7); //These are for spawning per recruits + sdrLnRec(i) = log(sdrRec(i)); + sdrMMB(i) = mfexp(sd_log_ssb(i)); + sdrLnRecMMB(i) = log(sdrRec(i)/sdrMMB(i)); + // sdrRec(i) = recruits(1,i); //These are for recruits + // sdrLnRec(i) = log(sdrRec(i)); + // sdrMMB(i) = mfexp(sd_log_ssb(i)); + // sdrLnRecMMB(i) = log(sdrRec(i)); + } + for ( int i = syr; i <= nyr-1; i++ ) sd_fbar(i) = mean(F(1,i)); + + // projection outcomes + sd_rbar = spr_rbar; + sd_ssbF0 = ssbF0; + sd_Bmsy = Bmsy; + sd_depl = spr_depl; + sd_ofl = spr_cofl; + + // Zero catch + F.initialize(); + for ( int h = 1; h <= nsex; h++ ) + for ( int i = syr; i <= nyr; i++ ) + for ( int j = 1; j <= nseason; j++ ) + for ( int l = 1; l <= nclass; l++) + F2(h,i,j,l) = 1.0e-10; + calc_total_mortality(); + calc_initial_numbers_at_length(); + update_population_numbers_at_length(); + sd_log_dyn_Bzero = log(calc_ssb())(syr+1,nyr); + sd_log_dyn_Bzero = (sd_log_ssb(syr+1,nyr)) - (sd_log_dyn_Bzero); + + // Actual catch + F = ftmp; + F2 = ftmp2; + calc_total_mortality(); + calc_initial_numbers_at_length(); + update_population_numbers_at_length(); + +// ===================================================================================================================================== + + /** + * @brief Calculate sdnr and MAR + **/ +FUNCTION void get_all_sdnr_MAR() + { + for ( int k = 1; k <= nSurveys; k++ ) + { + dvector stdtmp = cpue_sd(k) * 1.0 / cpue_lambda(k); + dvar_vector restmp = elem_div(log(elem_div(obs_cpue(k), pre_cpue(k))), stdtmp) + 0.5 * stdtmp; + sdnr_MAR_cpue(k) = calc_sdnr_MAR(value(restmp)); + } + for ( int k = 1; k <= nSizeComps; k++ ) + { + sdnr_MAR_lf(k) = calc_sdnr_MAR(value(d3_res_size_comps(k))); + } + //Francis_weights = calc_Francis_weights(); + } + +// --------------------------------------------------------------------------------------------------------- + + /** + * @brief find the standard deviation of the standardized residuals and their median + **/ +FUNCTION dvector calc_sdnr_MAR(dvector tmpVec) + { + dvector out(1,2); + dvector tmp = fabs(tmpVec); + dvector w = sort(tmp); + out(1) = std_dev(tmpVec); // sdnr + out(2) = w(floor(0.5*(size_count(w)+1))); // MAR + return out; + } + +FUNCTION dvector calc_sdnr_MAR(dmatrix tmpMat) + { + dvector tmpVec(1,size_count(tmpMat)); + dvector out(1,2); + int dmin,dmax; + dmin = 1; + for ( int ii = tmpMat.indexmin(); ii <= tmpMat.indexmax(); ii++ ) + { + dmax = dmin + size_count(tmpMat(ii)) - 1; + tmpVec(dmin,dmax) = tmpMat(ii).shift(dmin); + dmin = dmax + 1; + } + dvector tmp = fabs(tmpVec); + dvector w = sort(tmp); + out(1) = std_dev(tmpVec); // sdnr + out(2) = w(floor(0.5*(size_count(w)+1))); // MAR + return out; + } + +// ------------------------------------------------------------------------------------------------------------------------------------------------- + + /** + * @brief Calculate Francis weights + * @details this code based on equation TA1.8 in Francis(2011) should be changed so separate weights if by sex + * + * Produces the new weight that should be used. + **/ +FUNCTION dvector calc_Francis_weights() + { + int j,nobs; + double Obs, Pre, Var; + dvector lfwt(1,nSizeComps); + + for ( int k = 1; k <= nSizeComps; k++ ) + { + nobs = nSizeCompRows(k); + dvector resid(1,nobs); + j = 1; + resid.initialize(); + for ( int i = 1; i <= nSizeCompRows(k); i++ ) + { + cout << k << " " << i << endl; + cout << d3_obs_size_comps(k,i) << endl; + cout << d3_pre_size_comps(k,i) << endl; + cout << mid_points << endl; + Obs = sum(elem_prod(d3_obs_size_comps(k,i), mid_points)); + cout << Obs << endl; + Pre = sum(elem_prod(value(d3_pre_size_comps(k,i)), mid_points)); + Var = sum(elem_prod(value(d3_pre_size_comps(k,i)), square(mid_points))); + Var -= square(Pre); + resid(j++) = (Obs - Pre) / sqrt(Var * 1.0 / (size_comp_sample_size(k,i) * lf_lambda(k))); + } + lfwt(k) = 1.0 / (square(std_dev(resid)) * ((nobs - 1.0) / (nobs * 1.0))); + lfwt(k) *= lf_lambda(k); + } + return lfwt; + } + + /** + * @brief calculate effective sample size + * @details Calculate the effective sample size + * + * @param observed proportions + * @param predicted proportions + **/ + +// ===================================================================================================================================== + +FUNCTION CreateOutput + int Ipar,Jpar,Npar,NparEst; + + + cout << "here" << endl; + get_all_sdnr_MAR(); ///> Output specific to diagnostics + cout << "here" << endl; + + + cout << "+--------------------------+" << endl; + cout << "| Beginning report section |" << endl; + cout << "+--------------------------+" << endl; + OutFile1.close(); + OutFile1.open("Gmacsall.out"); + OutFile2.close(); + OutFile2.open("gmacs.rep"); + + // Likelihood summary + OutFile1 << "#Likelihoods_by_type (raw and weighted)" << endl; + OutFile1 << "Catch data : " << sum(nloglike(1)) << " " << sum(elem_prod(nloglike(1),catch_emphasis)) << endl; + OutFile1 << "Index data : " << sum(nloglike(2)) << " " << sum(elem_prod(nloglike(2),cpue_emphasis)) << endl; + OutFile1 << "Size data : " << sum(nloglike(3)) << " " << sum(elem_prod(nloglike(3),lf_emphasis)) << endl; + OutFile1 << "Stock recruitment : " << sum(nloglike(4)) << " " << sum(nloglike(4)) << endl; + OutFile1 << "Tagging data : " << sum(nloglike(5)) << " " << sum(nloglike(5)) << endl; + OutFile1 << "Penalties : " << sum(elem_prod(nlogPenalty,Penalty_emphasis)) << endl; + OutFile1 << "Priors : " << sum(priorDensity) << endl; + OutFile1 << "Initial size-structure : " << TempSS << endl; + OutFile1 << "Total : " << objfun << endl; + OutFile1 << endl; + + // Likelihood summary + OutFile1 << "#Likelihoods_by_type_and_fleet" << endl; + OutFile1 << "Catches" << endl; + OutFile1 << "Raw likelihood: " << nloglike(1) << endl; + OutFile1 << "Emphasis : " << catch_emphasis << endl; + OutFile1 << "Net likelihood: " << elem_prod(nloglike(1),catch_emphasis) << endl; + OutFile1 << "Index" << endl; + OutFile1 << "Raw likelihood: " << nloglike(2) << endl; + OutFile1 << "Emphasis : " << cpue_emphasis << endl; + OutFile1 << "Net likelihood: " << elem_prod(nloglike(2),cpue_emphasis) << endl; + OutFile1 << "Size-composition" << endl; + OutFile1 << "Raw likelihood: " << nloglike(3) << endl; + OutFile1 << "Emphasis : " << lf_emphasis << endl; + OutFile1 << "Net likelihood: " << elem_prod(nloglike(3),lf_emphasis) << endl; + OutFile1 << "Recruitment penalities" << endl; + OutFile1 << "Penalities : " << nloglike(4) << endl; + OutFile1 << endl; + + OutFile1 << "#Penalties" << endl; + OutFile1 << "1. Mean Fbar=0 : " << nlogPenalty(1) << " " << Penalty_emphasis(1) << " " << nlogPenalty(1)*Penalty_emphasis(1) << endl; + OutFile1 << "2. Mean Fdev : " << nlogPenalty(2) << " " << Penalty_emphasis(2) << " " << nlogPenalty(2)*Penalty_emphasis(2) << endl; + OutFile1 << "3. Mdevs : " << nlogPenalty(3) << " " << Penalty_emphasis(3) << " " << nlogPenalty(3)*Penalty_emphasis(3) << endl; + OutFile1 << "5. Rec_ini : " << nlogPenalty(5) << " " << Penalty_emphasis(5) << " " << nlogPenalty(5)*Penalty_emphasis(5) << endl; + OutFile1 << "6. Rec_dev : " << nlogPenalty(6) << " " << Penalty_emphasis(6) << " " << nlogPenalty(6)*Penalty_emphasis(6) << endl; + OutFile1 << "7. Sex ratio : " << nlogPenalty(7) << " " << Penalty_emphasis(7) << " " << nlogPenalty(7)*Penalty_emphasis(7) << endl; + OutFile1 << endl; + + // Estimated parameters + // ==================== + OutFile1 << "#Parameter_count Parameter_type Index Estimate Phase Lower_bound Upper_bound Penalty Standard_error Estimated_parameter_count" << endl; + + Npar = 0; NparEst = 0; + for (Ipar=1;Ipar<=ntheta;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : Theta " << Ipar << " : " << theta(Ipar) << " " << theta_phz(Ipar) << " "; + if (theta_phz(Ipar) > 0 & theta_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(theta(Ipar),theta_lb(Ipar),theta_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nGrwth;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : Growth " << Ipar << " : " << Grwth(Ipar) << " " << Grwth_phz(Ipar) << " "; + if (Grwth_phz(Ipar) > 0 & Grwth_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(Grwth(Ipar),Grwth_lb(Ipar),Grwth_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nslx_pars;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : nslx_pars " << Ipar << " : " << log_slx_pars(Ipar) << " " << slx_phzm(Ipar) << " "; + if (slx_phzm(Ipar) > 0 & slx_phzm(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_slx_pars(Ipar),slx_lb(Ipar),slx_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=NumAsympRet;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : NumAsympRet " << Ipar << " : " << Asymret(Ipar) << " " << AsympSel_phz(Ipar) << " "; + if (AsympSel_phz(Ipar) > 0 & AsympSel_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(Asymret(Ipar),AsympSel_lb(Ipar),AsympSel_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nfleet;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : log_fbar " << Ipar << " : " << log_fbar(Ipar) << " " << f_phz(Ipar) << " "; + if (f_phz(Ipar) > 0 & f_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_fbar(Ipar),-1000.0,1000.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nfleet;Ipar++) + for (Jpar=1;Jpar<=nFparams(Ipar);Jpar++) + { + Npar +=1; + OutFile1 << Npar << " : log_fdev " << Ipar << " : " << log_fdev(Ipar,Jpar) << " " << f_phz(Ipar) << " "; + if (f_phz(Ipar) > 0 & f_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_fdev(Ipar,Jpar),-1000.0,1000.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nfleet;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : log_foff " << Ipar << " : " << log_foff(Ipar) << " " << foff_phz(Ipar) << " "; + if (foff_phz(Ipar) > 0 & foff_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_foff(Ipar),-1000.0,1000.0); OutFile1 << priorDensity(NparEst) << " "<< ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nfleet;Ipar++) + for (Jpar=1;Jpar<=nYparams(Ipar);Jpar++) + { + Npar +=1; + OutFile1 << Npar << " : log_fdov " << Ipar << " : " << log_fdov(Ipar,Jpar) << " " << foff_phz(Ipar) << " "; + if (foff_phz(Ipar) > 0 & foff_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_fdov(Ipar,Jpar),-1000.0,1000.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nclass;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : rec_ini " << Ipar << " : " << rec_ini(Ipar) << " " << rec_ini_phz << " "; + if (rec_ini_phz > 0 & rdv_phz <= current_phase()) { NparEst +=1; CheckBounds(rec_ini(Ipar),-14.0,14.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=rdv_syr;Ipar<=rdv_eyr;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : rec_dev_est " << Ipar << " : " << rec_dev_est(Ipar) << " " << rdv_phz << " "; + if (rdv_phz > 0 & rdv_phz <= current_phase()) { NparEst +=1; CheckBounds(rec_dev_est(Ipar),-8.0,8.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=rdv_syr;Ipar<=rdv_eyr;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : logit_rec_prop_est " << Ipar << " : " << logit_rec_prop_est(Ipar) << " " << rec_prop_phz << " "; + if (rec_prop_phz > 0 & rec_prop_phz <= current_phase()) { NparEst +=1; CheckBounds(logit_rec_prop_est(Ipar),-100.0,100.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nMdev;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : m_dev_est " << Ipar << " : " << m_dev_est(Ipar) << " " << Mdev_phz(Ipar) << " "; + if (Mdev_phz(Ipar) > 0 & Mdev_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(m_dev_est(Ipar),Mdev_lb(Ipar),Mdev_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nSizeComps;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : log_vn " << Ipar << " : " << log_vn(Ipar) << " " << nvn_phz(Ipar) << " "; + if (nvn_phz(Ipar) > 0 & nvn_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_vn(Ipar),-1000.0,1000.0); OutFile1 << priorDensity(NparEst) << " " << ParsOut.sd(NparEst) << " " << NparEst; } + OutFile1 << endl; + } + for (Ipar=1;Ipar<=nSurveys;Ipar++) + { + Npar +=1; + OutFile1 << Npar << " : survey_q " << Ipar << " : " << survey_q(Ipar) << " " << q_phz(Ipar) << " "; + if (q_phz(Ipar) > 0 & q_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(survey_q(Ipar),q_lb(Ipar),q_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " < 0 & cv_phz(Ipar) <= current_phase()) { NparEst +=1; CheckBounds(log_add_cv(Ipar),log_add_cv_lb(Ipar),log_add_cv_ub(Ipar)); OutFile1 << priorDensity(NparEst) << " " < 1) REPORT(N_females); + REPORT(N_males_new); + if (nsex > 1) REPORT(N_females_new); + REPORT(N_males_old); + if (nsex > 1) REPORT(N_females_old); + REPORT(N_males_mature); + if (nsex > 1) REPORT(N_females_mature); + OutFile1 << endl; + + OutFile1 << "#--------------------------------------------------------------------------------------------" << endl; + OutFile1 << "#Molting and growth" << endl; + + REPORT(molt_increment); + REPORT(dPreMoltSize); + REPORT(iMoltIncSex); + REPORT(dMoltInc); + + // Molting probability + REPORT(molt_probability); ///> vector of molt probabilities + OutFile1 << "growth_transition" << endl; + for (int h=1; h <= nsex; h++) + for (int i=1;i<=nSizeIncVaries(h);i++) + { + OutFile1 << "#growth_matrix for (sex, increment_no) " << h << " " << i << endl; + OutFile1 << trans(growth_transition(h,i)) << endl; + } + + // Projection stuff + if ( last_phase() ) + { + OutFile1 << endl; + + OutFile1 << "#--------------------------------------------------------------------------------------------" << endl; + OutFile1 << "#Reference points" << endl; + + // calculate the reference points + calc_spr_reference_points2(1); + + OutFile1 << "#----------------------------" << endl; + OutFile1 << "#- Reference points and OFL -" << endl; + OutFile1 << "#----------------------------" << endl; + + REPORT(spr_syr); + REPORT(spr_nyr); + REPORT(spr_rbar); + REPORT(ssbF0); + REPORT(spr_bmsy); + REPORT(spr_depl); + OutFile1 << "SR_alpha_prj" << endl;; + OutFile1 << setw(15) << setprecision(8) << setfixed() << SR_alpha_prj << endl;; + OutFile1 << "SR_beta_prj" << endl; + OutFile1 << setw(15) << setprecision(8) << setfixed() << SR_beta_prj << endl; + REPORT(spr_fofl); + REPORT(sd_fmsy); + REPORT(sd_fofl); + REPORT(spr_cofl); + if (Compute_yield_prj==1) REPORT(spr_yield); + } + +// ===================================================================================================================================== + +// Andre done from here + +REPORT_SECTION + cout << "+--------------------------+" << endl; + cout << "| Beginning report section |" << endl; + cout << "+--------------------------+" << endl; + CreateOutput(); + save_gradients(gradients); + +// ===================================================================================================================================== + +RUNTIME_SECTION + maximum_function_evaluations 2000, 800, 1500, 25000, 25000 + convergence_criteria 1.e-8, 1.e-8, 1.e-8, 1.e-8, 1.e-8 + +// ===================================================================================================================================== + +GLOBALS_SECTION + /** + * @file gmacs.cpp + * @authors Steve Martell, Jim Ianelli, D'Arcy Webber + **/ + #include + #include + adstring like_names; + adstring prior_names; + #if defined __APPLE__ || defined __linux + #include "./include/libgmacs.h" + #endif + + #if defined _WIN32 || defined _WIN64 + #include "include\libgmacs.h" + #endif + + time_t start,finish; + long hour,minute,second; + double elapsed_time; + + ofstream OutFile1; + ofstream OutFile2; + + // Define objects for report file, echoinput, etc. + /** + \def report(object) + Prints name and value of \a object on ADMB report %ofstream file. + */ + #undef REPORT + #define REPORT(object) OutFile1 << #object "\n" << setw(8) \ + << setprecision(4) << setfixed() << object << endl; \ + OutFile2 << #object "\n" << setw(8) \ + << setprecision(4) << setfixed() << object << endl; + + /** + * \def COUT(object) + * Prints object to screen during runtime. + * cout < upper-Range*0.01) Status = 1; + OutFile1 << lower << " " << upper << " "; + if (Status == 1) OutFile1 << "*" << " "; + + RETURN_ARRAYS_DECREMENT(); + return (Status); + } + +// =============================================================================================== + +TOP_OF_MAIN_SECTION + time(&start); + arrmblsize = 50000000; + gradient_structure::set_GRADSTACK_BUFFER_SIZE(1.e7); + gradient_structure::set_CMPDIF_BUFFER_SIZE(5.e7); + gradient_structure::set_MAX_NVAR_OFFSET(5000); + gradient_structure::set_NUM_DEPENDENT_VARIABLES(5000); + gradient_structure::set_MAX_DLINKS(150000); + +// =============================================================================================== + +FINAL_SECTION + CreateOutput(); + + // Print run time statistics to the screen. + time(&finish); + elapsed_time = difftime(finish,start); + hour = long(elapsed_time)/3600; + minute = long(elapsed_time)%3600/60; + second = (long(elapsed_time)%3600)%60; + cout << endl << endl << "*******************************************" << endl; + cout << endl << endl << "-------------------------------------------" << endl; + cout << "--Start time: " << ctime(&start) << endl; + cout << "--Finish time: " << ctime(&finish) << endl; + cout << "--Runtime: "; + cout << hour << " hours, " << minute << " minutes, " << second << " seconds" << endl; + cout << "--Number of function evaluations: " << NfunCall << endl; + cout << "*******************************************" << endl; From c78418a33b450dd2620d5e45776393cc532be41b Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Mon, 22 Apr 2019 10:50:32 -0800 Subject: [PATCH 02/12] updates to read me file --- examples/smbkc_18a/projections/README.md | 69 ++++++++++++++++-------- 1 file changed, 48 insertions(+), 21 deletions(-) diff --git a/examples/smbkc_18a/projections/README.md b/examples/smbkc_18a/projections/README.md index 987a5c48..37aeabfc 100644 --- a/examples/smbkc_18a/projections/README.md +++ b/examples/smbkc_18a/projections/README.md @@ -1,37 +1,64 @@ -# smbkc +# Base model +From Andre with projection options available. +Model 1 from 2018a - see smbkc_18a/model_1 model for smbkc for 2018a model framework and setup are pulled from 'gmacs' # Current model from gmacs/examples/smbkc_18a/model_1 was model 3 from 2018 assessment -Use tpl file from Jie saved locally: -Local(C:\Users\kjpalof\Documents\SMBKC\breakpoint_SR\breakpoint_smbkc\model_1_2018a ) -Use this .tpl file to create gmacs.exe -also put this .tpl in projection folder +# GMACS version +gmacs.tpl file from Jie on 4-8-19 copied gmacs.exe from folder +(local only: C:\Users\kjpalof\Documents\SMBKC\breakpoint_SR\breakpoint_smbkc\model_1_2018a)) +gmacs.tpl in projections folder, version with Jie's edits 4-5-19 -# projections -Projection +# Projections +projections + 1 mean recruitment 1979-2017 + 2 ricker recruitment + 3 B-H recruitment + 4 mean recruitment 1996-2017 + 5 mean recruitment 1996-2017, Bmsy from 1996-2017 + 6 mean recruitment 1999-2008 + 7 mean recruitment 1989-2017 -1: mean recruitment 1978-2017 -2: ricker recruitment -3: B-H recruitment -4: mean recruitment 1996-2017 -5: mean recruitment 1996-2017, Bmsy 1996-2017 -6: mean recruitment 1999-2008 -7: mean recruitment 1989-2017 +a) No bycatch mortality, NO state harvest policy implemented +b) Bycatch mortality average last 5 years, NO state harvest policy implemented +c) No bycatch mortality, state harvest policy implemented +d) Bycatch mortality average last 5 years, state harvest policy implemented -Versions: -a) no bycath mortality / no State harvest control policy -b) bycath mortality / State harvest control policy -c) no bycath mortality / no State harvest control policy -d) bycath mortality / State harvest control policy + +# Summary +Bycatch mortality does NOT influence projection results +1 Assumes mean recruitment from the entire time series (1978-2017) +Tmin - under no directed fishing - 7.5 years + - F=0.18 not rebuilt + - State harvest policy implemented - T min = 11.5 years + +2 Assumes ricker stock recruit relationship +Tmin - under no directed fishing - 16.5 years + - F=0.18 not rebuilt + - State harvest policy implemented - T min = 28.5 years + +3 Assumes B-H stock recruit relationship +Tmin - under no directed fishing - 14.5 yeA ars + - F=0.18 not rebuilt + - State harvest policy implemented - T min = 23.5 years + +4 Assumes mean recruitment from the recent break in recruitment 1996-2017 +Not rebuilt in 50 years....no matter the F used. + e - 75 years from 4d + f - years from 4d - the 100 year projections aren't working...why? + +5 Assumes mean recruitment from recent break 1996-2017, also uses this time frame for BMSY proxy (1996-2017) +Tmin - under no directed fishing - 10.5 years + - F = 0.18 not rebuilt + - State harvest policy implemented - Tmin = 10.5 years -# Summary of results -Bycatch mortality does NOT influence projection results +# weighted combination # To do / Issues From df80250903184927c0b6b6813c8942f8520b4b17 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:16:01 -0800 Subject: [PATCH 03/12] functions and code for summarizing recovery probabilities from projections --- .../projections/KJP_risk_profile_general.R | 149 ++++++++++++++++++ examples/smbkc_18a/projections/functions.R | 77 +++++++++ examples/smbkc_18a/projections/helper.R | 22 +++ 3 files changed, 248 insertions(+) create mode 100644 examples/smbkc_18a/projections/KJP_risk_profile_general.R create mode 100644 examples/smbkc_18a/projections/functions.R create mode 100644 examples/smbkc_18a/projections/helper.R diff --git a/examples/smbkc_18a/projections/KJP_risk_profile_general.R b/examples/smbkc_18a/projections/KJP_risk_profile_general.R new file mode 100644 index 00000000..ae7c3371 --- /dev/null +++ b/examples/smbkc_18a/projections/KJP_risk_profile_general.R @@ -0,0 +1,149 @@ +## K Palof +## 4-09-19 + +# change the model and version with each projection output - should make this a function.... +#model <- "proj1" +#version <- "b" + +# load ------- +source("./projections/helper.R") +source("./projections/functions.R") # load function for summarising output from projection + +# creates .csv and .png files for each + +write_rec_prob(2, "proj1", "a") +write_rec_prob(2, "proj1", "b") +write_rec_prob(2, "proj1", "c") +write_rec_prob(2, "proj1", "d") + + +write_rec_prob(2, "proj5", "a") +write_rec_prob(2, "proj5", "b") +write_rec_prob(2, "proj5", "c") +write_rec_prob(2, "proj5", "d") + +write_rec_prob(2, "proj4", "a") +write_rec_prob(2, "proj4", "b") +write_rec_prob(2, "proj4", "c") +write_rec_prob(2, "proj4", "d") +write_rec_prob(2, "proj4", "e") +write_rec_prob(2, "proj4", "e_50yrs2") +write_rec_prob(2, "proj4", "f") + + +write_rec_prob(2, "proj3", "a") +write_rec_prob(2, "proj3", "b") +write_rec_prob(2, "proj3", "c") +write_rec_prob(2, "proj3", "d") + +write_rec_prob(2, "proj2", "a") +write_rec_prob(2, "proj2", "b") +write_rec_prob(2, "proj2", "c") +write_rec_prob(2, "proj2", "d") + +write_rec_prob(2, "proj7", "a") +write_rec_prob(2, "proj7", "b") +write_rec_prob(2, "proj7", "c") +write_rec_prob(2, "proj7", "d") + + +# bycatch comparison figure ----------------- +# use projection 1 version a and b to show no difference +no_byc <- read.csv("./projections/proj1/a/rec_prob_out_proj1a.csv") +byc <- read.csv("./projections/proj1/b/rec_prob_out_proj1b.csv") + +byc %>% + mutate(bycatch = "avg") -> byc + +no_byc %>% + mutate(bycatch = 'none') %>% + bind_rows(byc) -> byc_all + +byc_all %>% + filter(year == 1) -> year1 + +year1 %>% + mutate(year2 = year) -> year1 + +byc_all %>% + mutate(year2 = year + 1) %>% + bind_rows(year1) %>% + ggplot(aes(year2, recovery, group = V3)) + + geom_line(aes(group = V3, color = bycatch), lwd = 1.25) + + #geom_point(aes(group = V3, shape = FishMort), size = 1.5) + + #scale_shape_manual(name = "", values = c(16, 22)) + + scale_color_manual(name = "", values = c("darkgoldenrod3","blue3")) + + geom_hline(yintercept = 50, color = "red", lty = "dashed", lwd = 1.5) + + geom_vline(xintercept = 10, color = "blue", lty = 2, lwd = 1.5) + + ggtitle("Bycatch influence - projection 1 example") + + ylab("Probability of recovery") + + xlab("Year") + + theme(plot.title = element_text(hjust = 0.5)) -> plotA +ggsave(('./projections/figures/bycatch_mort_influence.png'), plotA, dpi = 800, + width = 7.5, height = 3.75) + +# weighted combinations --------------- +# bring in output csv here +# proj2 ricker and proj4 recent recruitment - all using 1978-2017 Bmsy +p2d <- read.csv("./projections/proj2/d/rec_prob_out_proj2d.csv") +p4d <- read.csv("./projections/proj4/d/rec_prob_out_proj4d.csv") + +p2d %>% + mutate(proj = 2, weight = 0.5) ->p2d + +p4d %>% + mutate(proj = 4, weight = 0.5) %>% + bind_rows(p2d) %>% + mutate(w_rec = recovery*weight) %>% + group_by(year, V3, FishMort) %>% + summarise(weighted_all = sum(w_rec)) %>% +ggplot(aes(year, weighted_all, group = V3)) + + geom_line() + + geom_point(aes(group = V3, shape = FishMort), size = 2) + + scale_shape_manual(name = "", values = c(16, 22)) + + geom_hline(yintercept = 50, color = "red", lty = "dashed", lwd = 1.5) + + geom_vline(xintercept = 10, color = "blue", lty = 2, lwd = 1.5) + + ggtitle("Weighted combination of proj2d and 4d - equal weighting") + + ylab("Probability of recovery") + + xlab("Year") + + ylim(0,100) + + theme(plot.title = element_text(hjust = 0.5)) +ggsave('./projections/figures/weighted_2d_4d_combo.png', dpi = 800, + width = 7.5, height = 3.75) + + + +# old code from Andre ------- +TheD <- read.table(paste0("./projections/", model, "/", version, "/mcoutPROJ.rep"))[,-c(4,5,6,7,8)] +Nyear <- length(TheD[1,])-4 +Nline <- length(TheD[,1]) +print(Nyear) +print(Nline) +n_prob_yr <- 2 + +# prob of recovery +for (Iline in 1:Nline) + TheD[Iline,5:(4 + Nyear)] <- TheD[Iline, 5:(4 + Nyear)] / TheD[Iline, 4] * 100 + + +TheD %>% + #mutate(ratio = f_sum(., V10, V59, V9)) %>% + mutate_at(vars(-V1, -V2, -V3, -V9), f_test) %>% + mutate(id = 1:n()) %>% + gather(year, value, -V1, -V2, -V3, -V9, -id) %>% + group_by(id) %>% + mutate(test = as.numeric(lead(value) + value ==2), + year = as.numeric(as.factor(year))) %>% + group_by(year, V3) %>% + summarise(recovery = sum(test) / n() * 100) %>% + mutate(FishMort = ifelse(V3 == 1, "F = 0", "F = 0.18")) %>% + ggplot(aes(year, recovery, group = V3)) + + geom_line() + + geom_point(aes(group = V3, shape = FishMort), size = 2) + + scale_shape_manual(name = "", values = c(16, 22)) + + geom_hline(yintercept = 50, color = "red", lty = "dashed", lwd = 1.5) + + geom_vline(xintercept = 10, color = "blue", lty = 2, lwd = 1.5) + + ggtitle(paste0(model, version)) + + ylab("Probability of recovery") + + xlab("Year") + + theme(plot.title = element_text(hjust = 0.5)) diff --git a/examples/smbkc_18a/projections/functions.R b/examples/smbkc_18a/projections/functions.R new file mode 100644 index 00000000..8d123049 --- /dev/null +++ b/examples/smbkc_18a/projections/functions.R @@ -0,0 +1,77 @@ +# k palof +# 04-09-2019 +# user created functions for smbkc projections + +f_sum <- function(data, col1, col2, div){ + col1 = enquo(col1) + col2 = enquo(col2) + div = enquo(div) + + data %>% + dplyr::select(!!col1:!!col2) %>% + apply(1, sum, na.rm=T) %>% + as.data.frame %>% + bind_cols(data) %>% + transmute(temp = . / !!div * 100) %>% + .$temp +} + + +f_test <- function(x){ + as.numeric(x > 100) +} + + +# function to output .csv and .png ------------------------ + +write_rec_prob <- function(n_prob_yr, model, version) { + TheD <- read.table(paste0("./projections/", model, "/", version, "/mcoutProj.rep"))[,-c(4,5,6,7,8)] + + Nyear <- length(TheD[1,])-4 + Nline <- length(TheD[,1]) + print(Nyear) + print(Nline) + + # prob of recovery + for (Iline in 1:Nline) + TheD[Iline,5:(4 + Nyear)] <- TheD[Iline, 5:(4 + Nyear)] / TheD[Iline, 4] * 100 + + + TheD %>% + #mutate(ratio = f_sum(., V10, V59, V9)) %>% + mutate_at(vars(-V1, -V2, -V3, -V9), f_test) %>% + mutate(id = 1:n()) %>% + gather(year, value, -V1, -V2, -V3, -V9, -id) %>% + group_by(id) %>% + mutate(test = as.numeric(lead(value) + value ==2), + year = as.numeric(as.factor(year))) %>% #this doesn't work if you get above V99 + group_by(year, V3) %>% + summarise(recovery = sum(test, na.rm = TRUE) / n() * 100) %>% + mutate(FishMort = ifelse(V3 == 1, "F = 0", "F = 0.18")) -> output + #write_csv("test.csv") + write_csv(output, paste0('./projections/', model, '/', version, '/rec_prob_out_', model, version, '.csv')) + + year1 <- output[1:2, ] + year1 %>% + mutate(year2 = year) -> year1 + + output %>% + mutate(year2 = year + 1) %>% + bind_rows(year1) %>% + filter(year2 <= Nyear) %>% + ggplot(aes(year2, recovery, group = V3)) + + geom_line() + + geom_point(aes(group = V3, shape = FishMort), size = 2) + + scale_shape_manual(name = "", values = c(16, 22)) + + geom_hline(yintercept = 50, color = "red", lty = "dashed", lwd = 1.5) + + geom_vline(xintercept = 10, color = "blue", lty = 2, lwd = 1.5) + + ggtitle(paste0(model, version)) + + ylab("Probability of recovery") + + xlab("Year") + + ylim(0,100) + + theme(plot.title = element_text(hjust = 0.5)) -> plotA + ggsave(paste0('./projections/figures/', model, '_', version, '_rec_prob.png'), plotA, dpi = 800, + width = 7.5, height = 3.75) + +} + diff --git a/examples/smbkc_18a/projections/helper.R b/examples/smbkc_18a/projections/helper.R new file mode 100644 index 00000000..ef30abfc --- /dev/null +++ b/examples/smbkc_18a/projections/helper.R @@ -0,0 +1,22 @@ +# generic packages and graphic settings. +# Katie Palof +# katie.palof@alaska.gov +# 2018-11-26 + +# load packages ----- +library(tidyverse) +library(reshape2) +library(scales) +library(extrafont) + +library(grid) +library(gridExtra) +library(FNGr) +library(scales) +library(cowplot) +library(readxl) +options(scipen=9999) # remove scientific notation + +loadfonts(device="win") +windowsFonts(Times=windowsFont("TT Times New Roman")) +theme_set(theme_sleek()) From a2a7dbf63b7fce4284e7a6c990f653be6b528f9f Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:16:25 -0800 Subject: [PATCH 04/12] markdown file summarising projections for May CPT meeting 2019 --- .../projections/SMBKC_rebuilding_may_2019.Rmd | 366 ++++++++++++++++++ 1 file changed, 366 insertions(+) create mode 100644 examples/smbkc_18a/projections/SMBKC_rebuilding_may_2019.Rmd diff --git a/examples/smbkc_18a/projections/SMBKC_rebuilding_may_2019.Rmd b/examples/smbkc_18a/projections/SMBKC_rebuilding_may_2019.Rmd new file mode 100644 index 00000000..de9b8fea --- /dev/null +++ b/examples/smbkc_18a/projections/SMBKC_rebuilding_may_2019.Rmd @@ -0,0 +1,366 @@ +--- +title: "Saint Matthew Island blue king crab stock status and rebuilding projections" +author: "| Katie Palof$^1$, Jie Zheng$^1$, Jim Ianelli$^2$, and Andre Punt$^3$ \n| + $^1$Alaska Department of Fish and Game, katie.palof@alaska.gov and jie.zheng@alaska.gov \n| $^2$NOAA, jim.ianelli@noaa.gov\n| + $^3$School of Aquatic and Fishery Science, University of Washington, aepunt@uw.edu\n" +date: "`r format(Sys.time(), '%B %Y')`" +output: + pdf_document: + highlight: zenburn + includes: null + toc: yes + +--- + +```{r global_options, include=FALSE } +library(knitr) +opts_chunk$set(fig.width = 12, fig.height = 7, echo = FALSE, warning = FALSE, message = FALSE, dpi=300) +#opts_chunk$set(fig.width = 12, fig.height = 7, echo = FALSE, warning = FALSE, message = FALSE, dpi=72) +``` + +```{r, load_packages, include=FALSE} +library(gmr) +library(xtable) +options(xtable.comment = FALSE) + +# The model specs +#mod_names <- c("Andre","2018 BTS","Reference","VAST","Fit surveys") #,"Gmacs M","Gmacs Francis","fit surveys") +mod_names <- c("base_model_1") #,"2018 BTS","Reference","VAST","Fit surveys") #,"Gmacs M","Gmacs Francis","fit surveys") +.MODELDIR = c("../smbkc_18a/model_1/") +.THEME = theme_bw(base_size = 12, base_family = "") +.OVERLAY = TRUE +.SEX = c("Aggregate","Male") +.FLEET = c("Pot","Trawl bycatch","Fixed bycatch","NMFS Trawl","ADF&G Pot") +.TYPE = c("Retained & Discarded","Retained","Discarded") +.SHELL = c("Aggregate") +.MATURITY = c("Aggregate") +.SEAS = c("1","2","3","4","5") + +# Read report file and create gmacs report object (a list): +fn <- paste0(.MODELDIR, "gmacs") +M <- lapply(fn, read_admb) +names(M) <- mod_names + +nmult_1 <- 1e+06 +nmult_2 <- 0.0004535923 * 1e+6 +#fn <- paste0(.MODELDIR[1], "gmacs") +#Mmatch <- lapply(fn, read_admb) +#names(Mmatch) <- c("SMBKC") + +fn <- paste0(.MODELDIR[1], "gmacs") +Mbase <- lapply(fn, read_admb) +names(Mbase) <- c("SMBKC") + +rinline <- function(code){ + html <- '``` `CODE` ```' + sub("CODE", code, html) +} + +ref_mod <- 1 # base +rec_mod <- 1 # base +mod_scen<- 3:5 +``` + + +# 2018 Executive Summary +1. **Stock**: Blue king crab, *Paralithodes platypus*, Saint Matthew Island (SMBKC), Alaska. + +2. **Catches**: Peak historical harvest was 4,288 t (9.454 million pounds) in 1983/84^[1983/84 refers to a fishing +year that extends from 1 July 1983 to 30 June 1984.]. +The fishery was closed for 10 years after the stock was declared +overfished in 1999. Fishing resumed in 2009/10 with a fishery-reported retained catch of 209 t (0.461 million +pounds), less than half the 529.3 t (1.167 million pound) TAC. +Following three more years of modest harvests +supported by a fishery catch per unit effort (CPUE) of around 10 crab per pot lift, the fishery was again closed in +2013/14 due to declining trawl-survey estimates of abundance and concerns about the health of the stock. +The directed +fishery resumed again in 2014/15 with a TAC of 300 t (0.655 million pounds), but the fishery performance was +relatively poor with a retained catch of 140 t (0.309 million pounds). +The retained catch in 2015/16 was even lower +at 48 t (0.105 million pounds) and the fishery has remained closed since 2016/17. + +3. **Stock biomass**: The 1978-2018 NMFS trawl survey mean biomass is 5,664 t with the 2018 value being the +5th lowest (1,731 t; the third lowest since 2000). This 2018 biomass of $\ge$ 90 mm carapace length (CL) male crab is +31% of the long term mean at 3.461 million pounds (with a CV of 28%). +The most recent 3-year average of the NMFS survey is 41% of the mean value, further indicating a decline in biomass compared to +historical survey estimates, notably in 2010 and 2011 that were over six times the current average. +The ADFG pot survey was repeated in 2018 and the relative biomass in this index was the lowest +in the time series (12% of the mean from the 11 surveys conducted since 1995). +The assessment model estimates dampen the interannual variability observed in the survey biomass and suggest that the stock (in survey biomass units) is presently at about +`r .get_cpue_df(Mbase) %>% filter(fleet==.FLEET[4]) %>% transmute(x = round(100*pred/mean(pred),0)) %>% tail(1) %>% .$x `% +of the long term model-predicted survey biomass average. The trend from these values suggests a slight decline. + +4. **Recruitment**: Recruitment is based on estimated number of male crab within the 90-104 mm CL size class +in each year. The 2018 trawl-survey area-swept estimate of 0.154 million male SMBKC in this size class is the third lowest in +the 41 years since 1978 and follows the lowest previously observed in 2017. The recent six-year (2013 - 2018) average recruitment +is only 45% of this mean. +In the pot-survey, the abundance of this size group in 2017 was also the second-lowest in the time series (22% of the mean for the +available pot-survey data) whereas in 2018 the value was the lowest observed at only 10% of the mean value. + +5. **Management performance**: In this assessment estimated total male catch is the sum of fishery-reported retained +catch, estimated male discard mortality in the directed fishery, and estimated male bycatch mortality in the groundfish +fisheries. Based on the reference model for SMBKC, the estimate for mature male biomass is below the minimum stock-size +threshold (MSST) in 2017/18 and is hence is in an "overfished" condition, despite fishery closures in the last two years +(and hence overfishing has not occurred) (Tables \ref{tab:status} and \ref{tab:status_pounds}). +Computations which indicate the relative impact of fishing +(i.e., the "dynamic $B_0$") suggests that the current spawning stock biomass has been reduced to 60% + + of what it would have been in the absence of +fishing, assuming the same level of recruitment as estimated. + +\begin{table}[ht] +\centering +\caption{Status and catch specifications (1000 t) for the reference model. +A - calculated from the assessment reviewed by the Crab Plan Team in September 2014, +B - calculated from the assessment reviewed by the Crab Plan Team in September 2015, +C - calculated from the assessment reviewed by the Crab Plan Team in September 2016, +D - calculated from the assessment reviewed by the Crab Plan Team in September 2017, +E - calculated from the 2018 model approved by the Crab Plan Team in September 2018 with GMACS modifications. +} +\label{tab:status} +\begin{tabular}{lccccccc} + \hline + & & Biomass & & Retained & Total & & \\ + Year & MSST & ($\mathit{MMB}_\text{mating}$) & TAC & catch & male catch & OFL & ABC \\ + \hline + 2013/14 & 1.50$^A$ & 3.01$^A$ & 0.00 & 0.00 & 0.00 & 0.56 & 0.45 \\ + 2014/15 & 1.86$^B$ & 2.48$^B$ & 0.30 & 0.14 & 0.15 & 0.43 & 0.34 \\ + 2015/16 & 1.84$^C$ & 2.11$^C$ & 0.19 & 0.05 & 0.05 & 0.28 & 0.22 \\ + 2016/17 & 1.97$^D$ & 2.23$^D$ & 0.00 & 0.00 & 0.05 & 0.14 & 0.11 \\ + 2017/18 & `r round(M[[rec_mod]]$spr_bmsy/1000 * 0.5, 2)`$^E$ & `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)-1]/1000, 2)`$^E$ & 0.00 & 0.00 & 0.05 & 0.12 & 0.10 \\ + 2018/19 & & `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)]/1000, 2)`$^E$ & & & & `r round(M[[rec_mod]]$spr_cofl/1000, 2)` & `r round(M[[rec_mod]]$spr_cofl/1000*0.8, 2)` \\ + \\ + \hline +\end{tabular} +\end{table} + +\begin{table}[ht] +\centering +\caption{Status and catch specifications (million pounds) for the reference model.} +\label{tab:status_pounds} +\begin{tabular}{lccccccc} + \hline + & & Biomass & & Retained & Total & & \\ + Year & MSST & ($\mathit{MMB}_\text{mating}$) & TAC & catch & male catch & OFL & ABC \\ + \hline + 2013/14 & 3.4$^A$ & 6.64$^A$ & 0.000 & 0.000 & 0.0006 & 1.24 & 0.99 \\ + 2014/15 & 4.1$^B$ & 5.47$^B$ & 0.655 & 0.309 & 0.329 & 0.94 & 0.75 \\ + 2015/16 & 4.1$^C$ & 4.65$^C$ & 0.419 & 0.110 & 0.110 & 0.62 & 0.49 \\ + 2016/17 & 4.3$^D$ & 4.91$^D$ & 0.410 & 0.000 & 0.000 & 0.31 & 0.25 \\ + 2017/18 & `r sprintf("%.1f", round(M[[rec_mod]]$spr_bmsy * 2204.62/1e6 * 0.5, 1))`$^E$ & `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)-1] * 2204.62/1e6, 2)`$^E$ & 0.41 & 0.000 & 0.000 & 0.27 & 0.22 \\ + 2018/19 & & `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)] * 2204.62/1e6, 2)`$^E$ & & & & `r round(M[[rec_mod]]$spr_cofl * 2204.62/1e6, 2)` & `r round(M[[rec_mod]]$spr_cofl * 2204.62/1e6*0.8, 2)` \\ + \hline +\end{tabular} +\end{table} + + + +6. **Basis for the OFL**: Estimated mature-male biomass (MMB) on 15 February is used as the measure of + biomass for this Tier 4 stock, with males measuring $\ge$ 105 mm CL + considered mature. The $B_\mathit{MSY}$ proxy is obtained by averaging estimated MMB over a specific reference period, and current CPT/SSC guidance recommends using the full assessment time frame as the default reference period (Table \ref{tab:ofl_basis}). +\begin{table}[ht] +\centering +\caption{Basis for the OFL (1000 t) from the reference model.} +\label{tab:ofl_basis} +\begin{tabular}{lcccccccc} + \hline + & & & Biomass & & & & & Natural \\ + Year & Tier & $B_\mathit{MSY}$ & ($\mathit{MMB}_\text{mating}$) & $B/B_\mathit{MSY}$ & $F_\mathit{OFL}$ & $\gamma$ & Basis for $B_\mathit{MSY}$ & mortality \\ + \hline + 2013/14 & 4b & 3.06 & 3.01 & 0.98 & 0.18 & 1 & 1978-2013 & 0.18 \\ + 2014/15 & 4b & 3.28 & 2.71 & 0.82 & 0.14 & 1 & 1978-2014 & 0.18 \\ + 2015/16 & 4b & 3.71 & 2.45 & 0.66 & 0.11 & 1 & 1978-2015 & 0.18 \\ + 2016/17 & 4b & 3.67 & 2.23 & 0.61 & 0.09 & 1 & 1978-2016 & 0.18 \\ + 2017/18 & 4b & 3.86 & 2.05 & 0.53 & 0.08 & 1 & 1978-2017 & 0.18 \\ + 2018/19 & 4b & `r round(M[[rec_mod]]$spr_bmsy/1000, 2)` & + `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)]/1000, 2)` & + `r round(M[[rec_mod]]$ssb[length(M[[rec_mod]]$ssb)]/M[[rec_mod]]$spr_bmsy, 2)` & + `r round(M[[rec_mod]]$sd_fofl[1], 3) ` & + 1 & 1978-2018 & 0.18 \\ + \hline +\end{tabular} +\end{table} + + +# Introduction +In 2018 the MMB for SMBKC fell below 50% of the $B_{MSY}$ proxy or the MSST, using average mature male biomass from 1978-2017. The stock was determined to be overfished (but overfishing is not occurring since the fishery has been closes the last two years) and a rebuilding plan is to be implemented. This document summarizes the current stock status, based on the 2018 assessment model, and projections done on rebuilding probabilities for the stock using the projections module developed for GMACS (A.Punt pers Comm). + +## Assessment model - current status +The 2018 assessment model was model 3 (2018 SAFE document, Zheng and Ianelli 2018), which used the 2017 model with updated data from both the NMFS trawl survey and the ADF&G pot survey. Some graphical output of this model is presented here with updates to the GMACS base code made since fall of 2018 (Figures 1-5). The model output is comparable to that presented in Sept. 2018. This model was used in the rebuilding projections. Variants of this model that will be presented in September include models 4 (VAST model prelim) and 5 (Fit survey) presented in 2018. Changes to the GMACS code have shown to have minor impacts on results, so work presented here focuses on rebuilding alternatives for consideration at the May 2019 meeting. + + +# Rebuilding projections +The rebuilding projections were performed using the projection module coded into GMACS in early 2019 (A. Punt per Comm). +A preliminary analysis of the rebuilding projections performed at the January crab plan team meeting by A.Punt concluded that bycatch mortality in this fishery was minor and that the rebuilding timeline was entirely dependent on assumptions of recruitment for the stock (Figure 6). + +Recruitment possibilities for the projection model include: Ricker, or Beverton-Holt stock recruit relationship and "random" recruitment. Stock-recruitment models (Ricker, Beverton-Holt) typically fit poorly for crab stocks, and this holds true for SMBKC (Figure 7). Projections using these stock recruitment relationships are still provided here for review since they scale recruitment to the current status of the stock. The "random"" recruitment option resamples historical recruitment estimates randomly, from a designated period for each projection iteration, such as the entire time series 1978 to 2017 as one example. This option assumes that recruitment is unrelated to stock size, but also relies on choosing the random draws from a biologically and environmentally representative time frame of past recruitment. + +Projections were performed to look at a range of combinations of recruitment, bycatch mortality, and implementation of the state harvest policy to determine the probability of recovery for each scenario. The SSC recommendations included a weighted combination of the projections based on their plausibility which is based on the life history and biology of the species. One such weighted combination is explored here but the biological plausibility of each projection assumption requires further feedback and discussion. + +\begin{table}[ht] +\centering +\caption{Projections performed with associated recruitment assumptions.} +\label{tab:projections} +\begin{tabular}{lcccccccc} + \hline + Projection & recruitment & $B_\mathit{MSY}$ proxy & recruitment years \\ + \hline + 1 & random recruitment & 1978-2017 & 1978-2017 \\ + 2 & Ricker & 1978-2017 & \\ + 3 & Beverton-Holt & 1978-2017 & \\ + 4 & random recruitment & 1978-2017 & 1996-2017 \\ + 5 & random recruitment & 1996-2017 & 1996-2017 \\ + 6 & random recruitment & 1978-2017 & 1999-2017 \\ + 7 & random recruitment & 1978-2017 & 1989-2017 \\ + \hline +\end{tabular} +\end{table} + +\begin{table}[ht] +\centering +\caption{Versions for each of the projections in Table \ref{tab:projections}.} +\label{tab:versions} +\begin{tabular}{lcccccccc} + \hline + Version & Bycatch mortality & SOA harvest policy \\ + \hline + a & 0 & no \\ + b & present (2013-2017) & no \\ + c & 0 & yes \\ + d & present (2013-2017) & yes \\ + \hline +\end{tabular} +\end{table} + + +## Results + +#### Bycatch mortality +Average values for recent (2013 - 2017) bycatch is insensitive to rebuilding time under any of the projections. Projections 1a and 1b, which use mean recruitment for the entire time series, clearly showed the lack of influence bycatch mortality had on recovery probability (Figure 6). Therefore, projections under version "a" were omitted from this presentation. + +#### State harvest policy +The implementation of the state harvest policy in the projections (version "d") affected rebuilding times in some projections. +In most cases the $T_{min}$ estimates increased. The implementation of the state harvest policy is less pessimistic than the maximum F = .0.18, hence we used version "d" for projection evaluations. + +#### Stock recruit relationship +Projections (projections 2 and 3) that used a stock recruit relationship assumed that the future recruitment was related to the current and future mature male biomass even though the fit to the stock recruit relationship was weak (Figure 7). These projections produced an intermediate estimate of $T_{min}$ compared to projections 1 and 4, under a Ricker S-R relationship the $T_{min}$= 16.5 years (F = 0) and under a Beverton-Holt S-R relationship the $T_{min}$= 14.5 years (F = 0) (Figure 8). When the state harvest policy is implemented these increase to 28.5 years and 23.5 years, respectively. + +#### Mean recruitment +Projections using "random" recruitment (projection 1) resampled from the entire time series (1978-2017) implied environmental conditions as being equal to this period. Under this hypothesis the probability of recovery produces a $T_{min}$ = 7.5 years under no directed fishery mortality (F = 0), and a $T_{min}$ = 11.5 years when the state harvest policy is implemented (Figure 9). The recruitment breakpoint analysis performed on this stock (Appendix A) suggested that recruitment conditions equal to the full period are unlikely and overly optimistic. + +Projections that use "random" recruitment (projection 4) for the recent period (1996-2017) specified by the recruitment breakpoint analysis (Appendix A) were more pessimistic. These suggested that the probability of recovery is low, even extending the projections out to 100 years, under recent recruitment conditions (Figure 10). The period for "random" recruitment was adjusted in this projection and the $B_{MSY}$ proxy was calculated using the entire time series (1978-2017), meaning that recent recruitment was inconsistent with the $B_{MSY}$ proxy that used the full time series. This projection was deemed as unrealistic, since under the current regime, $B_{MSY}$ would need to be consistent with the recent recruitment. + + +#### Updated $B_{MSY}$ proxy and mean recruitment +The recruitment breakpoint analysis suggested that a shift occurred in 1996. Both the "random" recruitment time period and the time period to calculate the $B_{MSY}$ proxy should reflect this (Table \ref{tab:B_msy proxy}). Projection 5 matches these two time frames, and shows that the $T_{min}$ for the probability of recovery to this new/current $B_{MSY}$ proxy is slightly more than 10 years, even under the state harvest policy implementation (Figure 11). This is due to the state harvest policy thresholds being based on past periods rather than having adopted to changes in $B_{MSY}$ proxy years. + + +#### Weighted combinations +When the initial projections were presented at the SSC meeting it was suggested that the Plan Team consider a combination of the projection hypotheses (which would be weighted according to their plausibility). One example of this would be a weighted combination of projection 2d and 4d, with a Ricker S-R relationship and current recruitment time series (1996-2017). This combination, under equal weighting, produces a scaled down version of the projection 2d outcome, with a resulting $T_{min}$ = 24 years (Figure 12). Other possible weighting combinations could be performed after further Plan Team and SSC consultation. + +## Discussion +The projections considered here produced a range of $T_{min}$ values (Table \ref{tab:t_min}), however, the question remains which option is the most biologically and environmentally plausible. The recruitment breakpoint analysis (Appendix A) suggested that recent recruitment (1996-2017) differed from the early part of the time series. Recruitment success for SMBKC, as with many crab species, is driven by environmental conditions. In the Bering Sea recent environmental conditions appear to be unfavorable for recruitment success for this stock, which may be due to the longer larval duration of blue king crab (reference). + +The most biologically and environmental plausible projection would be projection 4, which suggests that it would be unlikely for the stock to rebuild to the current $B_{MSY}$ proxy under current recruitment conditions. However, adjusting the $B_{MSY}$ time frame (projection 5) would allow the stock to rebuild to a a more representative $B_{MSY}$ that is based on current recruitment conditions. + +Combinations of the projections, weighted based on their plausibility, are possible but more justification and thought is needed to determine why these combinations would be a better representation of the current biology and environment of the stock. Also, reasoning for the weighting of projections in a combination would need to be established. + +\pagebreak + +## Tables + + +\begin{table}[ht] +\centering +\caption{$B_{MSY}$ proxy options for 2018 model 3, all Tier 4b.} +\label{tab:B_msy proxy} +\begin{tabular}{lcccccccc} + \hline + Year & Basis for $B_\mathit{MSY}$ & $B_\mathit{MSY}$ proxy & MSST & Biomass($MMB_{mating}$) & B/$B_{MSY}$ & $F_{OFL}$ & M \\ + \hline + 2018/19 & 1978-2017 & 3.48 & 1.74 & 1.09 & 0.31 & 0.041 & 0.18 \\ + 2018/19 & 1978-2017 & 2.03 & 1.015 & 1.08 & 0.53 & 0.087 & 0.18 \\ + \hline +\end{tabular} +\end{table} + + +\begin{table}[ht] +\centering +\caption{$T_{min}$ for each projection version d with no directed fishing (F=0).} +\label{tab:t_min} +\begin{tabular}{lcccccccc} + \hline + Projection & recruitment & $B_\mathit{MSY}$ proxy & recruitment yrs & $T_{min}$ \\ + \hline + 1 & random recruitment & 1978-2017 & 1978-2017 & 7.5 years \\ + 2 & ricker & 1979-2017 & & 16.5 years \\ + 3 & Beverton-Holt & 1979-2017 & & 14.5 years \\ + 4 & random recruitment & 1978-2017 & 1996-2017 & 100+ years \\ + 5 & random recruitment & 1996-2017 & 1996-2017 & 10.5 years \\ + 6 & random recruitment & 1978-2017 & 1999-2017 & 100+ years \\ + 7 & random recruitment & 1978-2017 & 1989-2017 & 10 years \\ + \hline +\end{tabular} +\end{table} + +\pagebreak + +## Figures +![](../smbkc_18a/model_1/figure/Fig3.png) + +Figure 1: Trawl and pot-survey stations used in the SMBKC stock assessment + +![](../smbkc_18a/model_1/figure/cpue_trawl.png) + +Figure 2: Fits to NMFS area-swept trawl estimates of total (>= 90mm) male survey biomass using the 2018 base model with GMACS updates. Error bars are plus and minus 2 standard deviations. + +![](../smbkc_18a/model_1/figure/cpue_pot.png) + +Figure 3: Fits to CPUE from the ADF&G pot surveys using the 2018 base model with GMACS updates. Error bars are plus and minus 2 standard deviations. + +![](../smbkc_18a/model_1/figure/recruitment_line.png) + +Figure 4: Estimated recruitment under the 2018 base model with GMACS updates, solid line is estimated average recruitment from the model; 1978-2018. + +![](../smbkc_18a/model_1/figure/ssb.png) + +Figure 5: Estimated mature male biomass (MMB) under the 2018 base model with GMACS updates; 1978-2018. + +\newpage + + +![](./figures/bycatch_mort_influence.png) + +Figure 6: Comparison on by catch mortality on rebuilding probabilities using F = 0 and F = 0.18 projections under assumptions of average recruitment for the entire time series. + +![](./figures/sr_relationship.png) + +Figure 7: Ricker stock-recruitment relationship with the associated variability for SMBKC. + +![](./figures/proj2_d_rec_prob.png) + +Figure 8: Recovery probability for projection 2d, using Ricker stock-recruit relationship, implementing the state harvest policy, and average recent bycatch mortality. + +![](./figures/proj1_d_rec_prob.png) + +Figure 9: Recovery probability for projection 1d, using mean recruitment (1978-2017), implementing the state harvest policy, and average recent bycatch mortality. + +![](./figures/proj4_d_rec_prob.png) + +Figure 10: Recovery probability for projection 4d, using mean recruitment (1996-2017), implementing the state harvest policy, and average recent bycatch mortality. + +![](./figures/proj5_d_rec_prob.png) + +Figure 11: Recovery probability for projection 5d, using an updated $B_{MSY}$ proxy (1996-2017), mean recruitment (1996-2017), implementing the state harvest policy, and average recent bycatch mortality. + +![](./figures/weighted_2d_4d_combo.png) + + +Figure 12: Recovery probability for a weighted combination of projections 2d and 4d using an equal probability of each. + + + + + + From 9e91198a81f8f61d20ba1c9790d1c6a373de78a4 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:16:53 -0800 Subject: [PATCH 05/12] projection files for proj1 --- .../projections/proj1/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj1/a/sm18.prj | 39 ++++++++++++ .../projections/proj1/b/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj1/b/sm18.prj | 39 ++++++++++++ .../projections/proj1/c/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj1/c/sm18.prj | 39 ++++++++++++ .../projections/proj1/d/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj1/d/sm18.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj1/sm18_1a.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj1/sm18_1b.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj1/sm18_1c.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj1/sm18_1d.prj | 39 ++++++++++++ 12 files changed, 552 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj1/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj1/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj1/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj1/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj1/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj1/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj1/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj1/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj1/sm18_1a.prj create mode 100644 examples/smbkc_18a/projections/proj1/sm18_1b.prj create mode 100644 examples/smbkc_18a/projections/proj1/sm18_1c.prj create mode 100644 examples/smbkc_18a/projections/proj1/sm18_1d.prj diff --git a/examples/smbkc_18a/projections/proj1/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj1/a/gmacs_in.prj new file mode 100644 index 00000000..881fbcb9 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +1e-005 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj1/a/sm18.prj b/examples/smbkc_18a/projections/proj1/a/sm18.prj new file mode 100644 index 00000000..871290e2 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj1/b/gmacs_in.prj new file mode 100644 index 00000000..7d6b5698 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +1e-005 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj1/b/sm18.prj b/examples/smbkc_18a/projections/proj1/b/sm18.prj new file mode 100644 index 00000000..0b1511fb --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj1/c/gmacs_in.prj new file mode 100644 index 00000000..d096cdca --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +1e-005 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj1/c/sm18.prj b/examples/smbkc_18a/projections/proj1/c/sm18.prj new file mode 100644 index 00000000..06833945 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj1/d/gmacs_in.prj new file mode 100644 index 00000000..0b67a042 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +1e-005 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj1/d/sm18.prj b/examples/smbkc_18a/projections/proj1/d/sm18.prj new file mode 100644 index 00000000..61851767 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/sm18_1a.prj b/examples/smbkc_18a/projections/proj1/sm18_1a.prj new file mode 100644 index 00000000..871290e2 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/sm18_1a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/sm18_1b.prj b/examples/smbkc_18a/projections/proj1/sm18_1b.prj new file mode 100644 index 00000000..0b1511fb --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/sm18_1b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/sm18_1c.prj b/examples/smbkc_18a/projections/proj1/sm18_1c.prj new file mode 100644 index 00000000..06833945 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/sm18_1c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj1/sm18_1d.prj b/examples/smbkc_18a/projections/proj1/sm18_1d.prj new file mode 100644 index 00000000..61851767 --- /dev/null +++ b/examples/smbkc_18a/projections/proj1/sm18_1d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From f7d08a398ea13464c7834172f3dc0c45bf18565a Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:17:05 -0800 Subject: [PATCH 06/12] projection files for proj2 --- .../projections/proj2/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj2/a/sm18.prj | 39 ++++++++++++ .../projections/proj2/b/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj2/b/sm18.prj | 39 ++++++++++++ .../projections/proj2/c/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj2/c/sm18.prj | 39 ++++++++++++ .../projections/proj2/d/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj2/d/sm18.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj2/sm18_2a.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj2/sm18_2b.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj2/sm18_2c.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj2/sm18_2d.prj | 39 ++++++++++++ 12 files changed, 552 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj2/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj2/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj2/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj2/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj2/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj2/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj2/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj2/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj2/sm18_2a.prj create mode 100644 examples/smbkc_18a/projections/proj2/sm18_2b.prj create mode 100644 examples/smbkc_18a/projections/proj2/sm18_2c.prj create mode 100644 examples/smbkc_18a/projections/proj2/sm18_2d.prj diff --git a/examples/smbkc_18a/projections/proj2/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj2/a/gmacs_in.prj new file mode 100644 index 00000000..d3a8a589 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +2 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj2/a/sm18.prj b/examples/smbkc_18a/projections/proj2/a/sm18.prj new file mode 100644 index 00000000..de2a6e68 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj2/b/gmacs_in.prj new file mode 100644 index 00000000..92161adc --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +2 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj2/b/sm18.prj b/examples/smbkc_18a/projections/proj2/b/sm18.prj new file mode 100644 index 00000000..735e7369 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj2/c/gmacs_in.prj new file mode 100644 index 00000000..c1245e44 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +2 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj2/c/sm18.prj b/examples/smbkc_18a/projections/proj2/c/sm18.prj new file mode 100644 index 00000000..398078b8 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj2/d/gmacs_in.prj new file mode 100644 index 00000000..3a5805ec --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +2 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj2/d/sm18.prj b/examples/smbkc_18a/projections/proj2/d/sm18.prj new file mode 100644 index 00000000..e9acd951 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/sm18_2a.prj b/examples/smbkc_18a/projections/proj2/sm18_2a.prj new file mode 100644 index 00000000..de2a6e68 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/sm18_2a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/sm18_2b.prj b/examples/smbkc_18a/projections/proj2/sm18_2b.prj new file mode 100644 index 00000000..735e7369 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/sm18_2b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/sm18_2c.prj b/examples/smbkc_18a/projections/proj2/sm18_2c.prj new file mode 100644 index 00000000..398078b8 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/sm18_2c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj2/sm18_2d.prj b/examples/smbkc_18a/projections/proj2/sm18_2d.prj new file mode 100644 index 00000000..e9acd951 --- /dev/null +++ b/examples/smbkc_18a/projections/proj2/sm18_2d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +2 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From dde1598cbb322fa34ea058ad9ed4e26adfd23abe Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:17:15 -0800 Subject: [PATCH 07/12] projection files for proj3 --- .../projections/proj3/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj3/a/sm18.prj | 39 ++++++++++++ .../projections/proj3/b/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj3/b/sm18.prj | 39 ++++++++++++ .../projections/proj3/c/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj3/c/sm18.prj | 39 ++++++++++++ .../projections/proj3/d/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj3/d/sm18.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj3/sm18_3a.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj3/sm18_3b.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj3/sm18_3c.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj3/sm18_3d.prj | 39 ++++++++++++ 12 files changed, 552 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj3/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj3/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj3/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj3/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj3/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj3/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj3/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj3/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj3/sm18_3a.prj create mode 100644 examples/smbkc_18a/projections/proj3/sm18_3b.prj create mode 100644 examples/smbkc_18a/projections/proj3/sm18_3c.prj create mode 100644 examples/smbkc_18a/projections/proj3/sm18_3d.prj diff --git a/examples/smbkc_18a/projections/proj3/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj3/a/gmacs_in.prj new file mode 100644 index 00000000..6049513b --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +3 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj3/a/sm18.prj b/examples/smbkc_18a/projections/proj3/a/sm18.prj new file mode 100644 index 00000000..4793c24a --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj3/b/gmacs_in.prj new file mode 100644 index 00000000..b10d7e99 --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +3 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj3/b/sm18.prj b/examples/smbkc_18a/projections/proj3/b/sm18.prj new file mode 100644 index 00000000..ec2faac7 --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj3/c/gmacs_in.prj new file mode 100644 index 00000000..53f4cd7f --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +3 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj3/c/sm18.prj b/examples/smbkc_18a/projections/proj3/c/sm18.prj new file mode 100644 index 00000000..662ac4aa --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj3/d/gmacs_in.prj new file mode 100644 index 00000000..0d897624 --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +3 +# Age_at_rec_prj +7 +# prj_futRec_syr +1978 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj3/d/sm18.prj b/examples/smbkc_18a/projections/proj3/d/sm18.prj new file mode 100644 index 00000000..87caf01f --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/sm18_3a.prj b/examples/smbkc_18a/projections/proj3/sm18_3a.prj new file mode 100644 index 00000000..4793c24a --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/sm18_3a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/sm18_3b.prj b/examples/smbkc_18a/projections/proj3/sm18_3b.prj new file mode 100644 index 00000000..ec2faac7 --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/sm18_3b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/sm18_3c.prj b/examples/smbkc_18a/projections/proj3/sm18_3c.prj new file mode 100644 index 00000000..662ac4aa --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/sm18_3c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj3/sm18_3d.prj b/examples/smbkc_18a/projections/proj3/sm18_3d.prj new file mode 100644 index 00000000..87caf01f --- /dev/null +++ b/examples/smbkc_18a/projections/proj3/sm18_3d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +3 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1978 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From 766d3d85b0a467fa840995c06452d4b4837c846a Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:17:27 -0800 Subject: [PATCH 08/12] projection files for proj4 --- .../projections/proj4/a/gmacs_in.prj | 60 +++++++ .../smbkc_18a/projections/proj4/a/sm18.prj | 39 +++++ .../projections/proj4/b/gmacs_in.prj | 60 +++++++ .../smbkc_18a/projections/proj4/b/sm18.prj | 39 +++++ .../projections/proj4/c/gmacs_in.prj | 60 +++++++ .../smbkc_18a/projections/proj4/c/sm18.prj | 39 +++++ .../projections/proj4/d/gmacs_in.prj | 60 +++++++ .../smbkc_18a/projections/proj4/d/sm18.prj | 39 +++++ .../projections/proj4/e_75yr/gmacs_in.prj | 60 +++++++ .../proj4/e_75yr/rec_prob_out_proj4e.csv | 151 ++++++++++++++++++ .../projections/proj4/e_75yr/sm18.prj | 39 +++++ .../smbkc_18a/projections/proj4/sm18_4a.prj | 39 +++++ .../smbkc_18a/projections/proj4/sm18_4b.prj | 39 +++++ .../smbkc_18a/projections/proj4/sm18_4c.prj | 39 +++++ .../smbkc_18a/projections/proj4/sm18_4d.prj | 39 +++++ .../smbkc_18a/projections/proj4/sm18_4e.prj | 39 +++++ 16 files changed, 841 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj4/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj4/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj4/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj4/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj4/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj4/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj4/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj4/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj4/e_75yr/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj4/e_75yr/rec_prob_out_proj4e.csv create mode 100644 examples/smbkc_18a/projections/proj4/e_75yr/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj4/sm18_4a.prj create mode 100644 examples/smbkc_18a/projections/proj4/sm18_4b.prj create mode 100644 examples/smbkc_18a/projections/proj4/sm18_4c.prj create mode 100644 examples/smbkc_18a/projections/proj4/sm18_4d.prj create mode 100644 examples/smbkc_18a/projections/proj4/sm18_4e.prj diff --git a/examples/smbkc_18a/projections/proj4/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj4/a/gmacs_in.prj new file mode 100644 index 00000000..8b4359ea --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj4/a/sm18.prj b/examples/smbkc_18a/projections/proj4/a/sm18.prj new file mode 100644 index 00000000..e9059e75 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj4/b/gmacs_in.prj new file mode 100644 index 00000000..3c6cee5a --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj4/b/sm18.prj b/examples/smbkc_18a/projections/proj4/b/sm18.prj new file mode 100644 index 00000000..83c20340 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj4/c/gmacs_in.prj new file mode 100644 index 00000000..82fe7c2b --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj4/c/sm18.prj b/examples/smbkc_18a/projections/proj4/c/sm18.prj new file mode 100644 index 00000000..1c09a590 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj4/d/gmacs_in.prj new file mode 100644 index 00000000..dbe97797 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj4/d/sm18.prj b/examples/smbkc_18a/projections/proj4/d/sm18.prj new file mode 100644 index 00000000..02d5fe3d --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/e_75yr/gmacs_in.prj b/examples/smbkc_18a/projections/proj4/e_75yr/gmacs_in.prj new file mode 100644 index 00000000..2b03864f --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/e_75yr/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +1 +# pyr +2092 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +100000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj4/e_75yr/rec_prob_out_proj4e.csv b/examples/smbkc_18a/projections/proj4/e_75yr/rec_prob_out_proj4e.csv new file mode 100644 index 00000000..c78a4c21 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/e_75yr/rec_prob_out_proj4e.csv @@ -0,0 +1,151 @@ +year,V3,recovery,FishMort +1,1,0,F = 0 +1,2,0,F = 0.18 +2,1,0,F = 0 +2,2,0,F = 0.18 +3,1,0,F = 0 +3,2,0,F = 0.18 +4,1,0,F = 0 +4,2,0,F = 0.18 +5,1,0,F = 0 +5,2,0,F = 0.18 +6,1,0.05,F = 0 +6,2,0,F = 0.18 +7,1,0.05,F = 0 +7,2,0,F = 0.18 +8,1,0.1,F = 0 +8,2,0,F = 0.18 +9,1,0,F = 0 +9,2,0,F = 0.18 +10,1,0.15,F = 0 +10,2,0,F = 0.18 +11,1,0.5,F = 0 +11,2,0,F = 0.18 +12,1,0.75,F = 0 +12,2,0,F = 0.18 +13,1,1,F = 0 +13,2,0,F = 0.18 +14,1,1.0999999999999999,F = 0 +14,2,0,F = 0.18 +15,1,1.25,F = 0 +15,2,0,F = 0.18 +16,1,1.0999999999999999,F = 0 +16,2,0,F = 0.18 +17,1,1.3,F = 0 +17,2,0,F = 0.18 +18,1,1.8499999999999999,F = 0 +18,2,0,F = 0.18 +19,1,2,F = 0 +19,2,0,F = 0.18 +20,1,2,F = 0 +20,2,0,F = 0.18 +21,1,2.1999999999999997,F = 0 +21,2,0,F = 0.18 +22,1,2.1999999999999997,F = 0 +22,2,0,F = 0.18 +23,1,2.5,F = 0 +23,2,0,F = 0.18 +24,1,2.35,F = 0 +24,2,0,F = 0.18 +25,1,1.9,F = 0 +25,2,0,F = 0.18 +26,1,1.95,F = 0 +26,2,0,F = 0.18 +27,1,1.8499999999999999,F = 0 +27,2,0,F = 0.18 +28,1,2.1999999999999997,F = 0 +28,2,0,F = 0.18 +29,1,2.55,F = 0 +29,2,0,F = 0.18 +30,1,2.9000000000000004,F = 0 +30,2,0,F = 0.18 +31,1,2.45,F = 0 +31,2,0,F = 0.18 +32,1,3.05,F = 0 +32,2,0,F = 0.18 +33,1,3,F = 0 +33,2,0,F = 0.18 +34,1,2.9499999999999997,F = 0 +34,2,0,F = 0.18 +35,1,2.6,F = 0 +35,2,0,F = 0.18 +36,1,2.45,F = 0 +36,2,0,F = 0.18 +37,1,2.55,F = 0 +37,2,0,F = 0.18 +38,1,2.9000000000000004,F = 0 +38,2,0,F = 0.18 +39,1,2.7,F = 0 +39,2,0,F = 0.18 +40,1,2.45,F = 0 +40,2,0,F = 0.18 +41,1,2.35,F = 0 +41,2,0,F = 0.18 +42,1,2.4,F = 0 +42,2,0,F = 0.18 +43,1,2,F = 0 +43,2,0,F = 0.18 +44,1,2.35,F = 0 +44,2,0,F = 0.18 +45,1,2.35,F = 0 +45,2,0,F = 0.18 +46,1,2.45,F = 0 +46,2,0,F = 0.18 +47,1,2.8000000000000003,F = 0 +47,2,0,F = 0.18 +48,1,2.4,F = 0 +48,2,0,F = 0.18 +49,1,3.05,F = 0 +49,2,0,F = 0.18 +50,1,2.8000000000000003,F = 0 +50,2,0,F = 0.18 +51,1,2.75,F = 0 +51,2,0,F = 0.18 +52,1,2.7,F = 0 +52,2,0,F = 0.18 +53,1,2.4,F = 0 +53,2,0,F = 0.18 +54,1,2.65,F = 0 +54,2,0,F = 0.18 +55,1,2.85,F = 0 +55,2,0,F = 0.18 +56,1,2.65,F = 0 +56,2,0,F = 0.18 +57,1,1.95,F = 0 +57,2,0,F = 0.18 +58,1,2.1999999999999997,F = 0 +58,2,0,F = 0.18 +59,1,2.1999999999999997,F = 0 +59,2,0,F = 0.18 +60,1,2.1,F = 0 +60,2,0,F = 0.18 +61,1,2.25,F = 0 +61,2,0,F = 0.18 +62,1,2.4,F = 0 +62,2,0,F = 0.18 +63,1,2.3,F = 0 +63,2,0,F = 0.18 +64,1,2.3,F = 0 +64,2,0,F = 0.18 +65,1,2.6,F = 0 +65,2,0,F = 0.18 +66,1,3,F = 0 +66,2,0,F = 0.18 +67,1,2.85,F = 0 +67,2,0,F = 0.18 +68,1,2.45,F = 0 +68,2,0,F = 0.18 +69,1,2.55,F = 0 +69,2,0,F = 0.18 +70,1,2.25,F = 0 +70,2,0,F = 0.18 +71,1,2.45,F = 0 +71,2,0,F = 0.18 +72,1,2.65,F = 0 +72,2,0,F = 0.18 +73,1,2.75,F = 0 +73,2,0,F = 0.18 +74,1,2.75,F = 0 +74,2,0,F = 0.18 +75,1,0,F = 0 +75,2,0,F = 0.18 diff --git a/examples/smbkc_18a/projections/proj4/e_75yr/sm18.prj b/examples/smbkc_18a/projections/proj4/e_75yr/sm18.prj new file mode 100644 index 00000000..3b75c649 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/e_75yr/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +1 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2092 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +100000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/sm18_4a.prj b/examples/smbkc_18a/projections/proj4/sm18_4a.prj new file mode 100644 index 00000000..e9059e75 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/sm18_4a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/sm18_4b.prj b/examples/smbkc_18a/projections/proj4/sm18_4b.prj new file mode 100644 index 00000000..83c20340 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/sm18_4b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/sm18_4c.prj b/examples/smbkc_18a/projections/proj4/sm18_4c.prj new file mode 100644 index 00000000..1c09a590 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/sm18_4c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/sm18_4d.prj b/examples/smbkc_18a/projections/proj4/sm18_4d.prj new file mode 100644 index 00000000..02d5fe3d --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/sm18_4d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj4/sm18_4e.prj b/examples/smbkc_18a/projections/proj4/sm18_4e.prj new file mode 100644 index 00000000..c1b6f137 --- /dev/null +++ b/examples/smbkc_18a/projections/proj4/sm18_4e.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2117 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +100000 # 10000 + +## eof +9999 + From 72ef8577ecdba4993c42943c4e7d32ef8d196b39 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:18:18 -0800 Subject: [PATCH 09/12] projection files for proj5 --- .../projections/proj5/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj5/a/sm18.prj | 39 ++++++++++++ .../projections/proj5/b/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj5/b/sm18.prj | 39 ++++++++++++ .../projections/proj5/c/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj5/c/sm18.prj | 39 ++++++++++++ .../projections/proj5/d/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj5/d/sm18.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj5/sm18_5a.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj5/sm18_5b.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj5/sm18_5c.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj5/sm18_5d.prj | 39 ++++++++++++ 12 files changed, 552 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj5/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj5/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj5/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj5/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj5/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj5/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj5/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj5/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj5/sm18_5a.prj create mode 100644 examples/smbkc_18a/projections/proj5/sm18_5b.prj create mode 100644 examples/smbkc_18a/projections/proj5/sm18_5c.prj create mode 100644 examples/smbkc_18a/projections/proj5/sm18_5d.prj diff --git a/examples/smbkc_18a/projections/proj5/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj5/a/gmacs_in.prj new file mode 100644 index 00000000..662a2eef --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1996 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj5/a/sm18.prj b/examples/smbkc_18a/projections/proj5/a/sm18.prj new file mode 100644 index 00000000..9fb48a2f --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj5/b/gmacs_in.prj new file mode 100644 index 00000000..bc717a6c --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1996 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj5/b/sm18.prj b/examples/smbkc_18a/projections/proj5/b/sm18.prj new file mode 100644 index 00000000..843adff9 --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj5/c/gmacs_in.prj new file mode 100644 index 00000000..62c1bc3c --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1996 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj5/c/sm18.prj b/examples/smbkc_18a/projections/proj5/c/sm18.prj new file mode 100644 index 00000000..eca9d09f --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj5/d/gmacs_in.prj new file mode 100644 index 00000000..f1e1d7ea --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1996 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1996 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj5/d/sm18.prj b/examples/smbkc_18a/projections/proj5/d/sm18.prj new file mode 100644 index 00000000..49e4efd0 --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/sm18_5a.prj b/examples/smbkc_18a/projections/proj5/sm18_5a.prj new file mode 100644 index 00000000..9fb48a2f --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/sm18_5a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/sm18_5b.prj b/examples/smbkc_18a/projections/proj5/sm18_5b.prj new file mode 100644 index 00000000..843adff9 --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/sm18_5b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/sm18_5c.prj b/examples/smbkc_18a/projections/proj5/sm18_5c.prj new file mode 100644 index 00000000..eca9d09f --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/sm18_5c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj5/sm18_5d.prj b/examples/smbkc_18a/projections/proj5/sm18_5d.prj new file mode 100644 index 00000000..49e4efd0 --- /dev/null +++ b/examples/smbkc_18a/projections/proj5/sm18_5d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1996 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1996 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From 25f71154ff52c0dcdd3abae54f1ca9a007d70091 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:18:29 -0800 Subject: [PATCH 10/12] projection files for proj6 --- .../projections/proj6/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj6/sm18_6a.prj | 39 ++++++++++++ 2 files changed, 99 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj6/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj6/sm18_6a.prj diff --git a/examples/smbkc_18a/projections/proj6/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj6/a/gmacs_in.prj new file mode 100644 index 00000000..e7778bbb --- /dev/null +++ b/examples/smbkc_18a/projections/proj6/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1999 +# prj_futRec_nyr +2008 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj6/sm18_6a.prj b/examples/smbkc_18a/projections/proj6/sm18_6a.prj new file mode 100644 index 00000000..6f702710 --- /dev/null +++ b/examples/smbkc_18a/projections/proj6/sm18_6a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1999 2008 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From 72062ffd7e1c697c173ff9a6210c9a8917da6ded Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:18:44 -0800 Subject: [PATCH 11/12] projection files for proj7 --- .../projections/proj7/a/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj7/a/sm18.prj | 39 ++++++++++++ .../projections/proj7/b/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj7/b/sm18.prj | 39 ++++++++++++ .../projections/proj7/c/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj7/c/sm18.prj | 39 ++++++++++++ .../projections/proj7/d/gmacs_in.prj | 60 +++++++++++++++++++ .../smbkc_18a/projections/proj7/d/sm18.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj7/sm18_7a.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj7/sm18_7b.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj7/sm18_7c.prj | 39 ++++++++++++ .../smbkc_18a/projections/proj7/sm18_7d.prj | 39 ++++++++++++ 12 files changed, 552 insertions(+) create mode 100644 examples/smbkc_18a/projections/proj7/a/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj7/a/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj7/b/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj7/b/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj7/c/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj7/c/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj7/d/gmacs_in.prj create mode 100644 examples/smbkc_18a/projections/proj7/d/sm18.prj create mode 100644 examples/smbkc_18a/projections/proj7/sm18_7a.prj create mode 100644 examples/smbkc_18a/projections/proj7/sm18_7b.prj create mode 100644 examples/smbkc_18a/projections/proj7/sm18_7c.prj create mode 100644 examples/smbkc_18a/projections/proj7/sm18_7d.prj diff --git a/examples/smbkc_18a/projections/proj7/a/gmacs_in.prj b/examples/smbkc_18a/projections/proj7/a/gmacs_in.prj new file mode 100644 index 00000000..8ff1d33f --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/a/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1989 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj7/a/sm18.prj b/examples/smbkc_18a/projections/proj7/a/sm18.prj new file mode 100644 index 00000000..cbe4d450 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/a/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/b/gmacs_in.prj b/examples/smbkc_18a/projections/proj7/b/gmacs_in.prj new file mode 100644 index 00000000..a34c65f3 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/b/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1989 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +0 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj7/b/sm18.prj b/examples/smbkc_18a/projections/proj7/b/sm18.prj new file mode 100644 index 00000000..cdff6292 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/b/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/c/gmacs_in.prj b/examples/smbkc_18a/projections/proj7/c/gmacs_in.prj new file mode 100644 index 00000000..7a6d764b --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/c/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +0 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1989 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj7/c/sm18.prj b/examples/smbkc_18a/projections/proj7/c/sm18.prj new file mode 100644 index 00000000..07d7e176 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/c/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/d/gmacs_in.prj b/examples/smbkc_18a/projections/proj7/d/gmacs_in.prj new file mode 100644 index 00000000..770f9815 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/d/gmacs_in.prj @@ -0,0 +1,60 @@ +# Calc_MSY +1 +# Ffixed + 0 1 1 1 1 +# spr_syr +1978 +# spr_nyr +2017 +# spr_aveF_syr +2013 +# spr_aveF_nyr +2017 +# spr_target +0.35 +# OFLTier +4 +# OFLalpha +0.1 +# OFLbeta +0.25 +# OFLgamma +1 +# ABCBuffer +0.8 +# Compute_yield_prj +0 +# pyr +2067 +# prj_Nstrat +2 +# prj_lowF +0 +# prj_hiF +0.18 +# prj_bycatch_on +1 +# prj_replicates +2 +# Fixed_prj_Bmsy +-3423.8 +# Stock_rec_prj +1 +# Age_at_rec_prj +7 +# prj_futRec_syr +1989 +# prj_futRec_nyr +2017 +# SigmaR_prj +0.6 +# Apply_HCR_prj +1 +# MeanWStateMature +0.00172963 +# MeanWStateLegal +0.00193093 +# max_prj +10000 +# eof_prj +9999 diff --git a/examples/smbkc_18a/projections/proj7/d/sm18.prj b/examples/smbkc_18a/projections/proj7/d/sm18.prj new file mode 100644 index 00000000..8b0d3a2a --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/d/sm18.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/sm18_7a.prj b/examples/smbkc_18a/projections/proj7/sm18_7a.prj new file mode 100644 index 00000000..cbe4d450 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/sm18_7a.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/sm18_7b.prj b/examples/smbkc_18a/projections/proj7/sm18_7b.prj new file mode 100644 index 00000000..cdff6292 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/sm18_7b.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +0 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/sm18_7c.prj b/examples/smbkc_18a/projections/proj7/sm18_7c.prj new file mode 100644 index 00000000..07d7e176 --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/sm18_7c.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +0 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + diff --git a/examples/smbkc_18a/projections/proj7/sm18_7d.prj b/examples/smbkc_18a/projections/proj7/sm18_7d.prj new file mode 100644 index 00000000..8b0d3a2a --- /dev/null +++ b/examples/smbkc_18a/projections/proj7/sm18_7d.prj @@ -0,0 +1,39 @@ +1 # Do not compute MSY (1=Yes) +0 1 1 1 1 # Set to 0 if F35% applied to this fleet; 1 if F is to be fixed +1978 2017 # First and last year for average recruitment/MMB for Bspr calculation (Tier 3 or Tier 4) +2013 2017 # First and last year for average F for discards (computing reference points and projections) + +# OFL specifications +0.35 # Target SPR ratio for Bmsy proxy. +4 # Tier +0.10 # Alpha (cut-off) +0.25 # Beta (limit) +1.00 # Gamma +0.80 # ABC-OFL buffer +0 # Produce a yield curve (1=yes; 2=no) + +# Projection material +2067 # 2067 # Last year of projection +2 # Number of strategies (0 for no projections) +0 0.18 # Range of F values +1 # 0 for no mortality for non-directed fleets (see input #1 above); 1=Yes +2 # Mcmc replicates per draw +-3423.8 # Fixed BMSY (negative number for replicate-specific) + +1 # 2 # Stock-recruitment option (1=Mean Rec;2=Ricker;3=Beverton-Holt) +7 # Time to recruitment +1989 2017 # First and last years for generating future recruitment (only used if Stock_recruitment option = 1) +#1999 2008 # First and last years for generating future recruitment +0.6 #0.00001 # 0.6 # SigmaR (only used if Stock_recruitment option = 2) [0.6] + +# State strategy +1 # 1 # Apply strategies [OFL, ABC] (1=yes;0=no) +0.001729630 # Mean weight to use (mature) +0.001930932 # Mean weight to use (legal) + +# Stop after XX mcdraws +10000 # 10000 + +## eof +9999 + From 40a5cf29f6097263f6ab44e8e1768f6b5e811753 Mon Sep 17 00:00:00 2001 From: Katie Palof Date: Wed, 24 Apr 2019 15:19:53 -0800 Subject: [PATCH 12/12] doc for smbkc recruitment breakpt analysis. code is not in this respository. --- .../Appendix 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